From 598c9325385cd890aaae4389f31520097043e2c6 Mon Sep 17 00:00:00 2001 From: Mahdieh Pirmoradian <74245613+MahdiehPirmoradian@users.noreply.github.com> Date: Sun, 14 Jan 2024 06:05:35 +0100 Subject: [PATCH 01/16] Update and rename implement-sdr-representation-samples.md to working-with-sdrs.md Minor editing --- ...tation-samples.md => working-with-sdrs.md} | 34 +++++++++---------- 1 file changed, 17 insertions(+), 17 deletions(-) rename source/NeoCortexUtils/docs/{implement-sdr-representation-samples.md => working-with-sdrs.md} (87%) diff --git a/source/NeoCortexUtils/docs/implement-sdr-representation-samples.md b/source/NeoCortexUtils/docs/working-with-sdrs.md similarity index 87% rename from source/NeoCortexUtils/docs/implement-sdr-representation-samples.md rename to source/NeoCortexUtils/docs/working-with-sdrs.md index 0a3d32c17..a8c73cb36 100644 --- a/source/NeoCortexUtils/docs/implement-sdr-representation-samples.md +++ b/source/NeoCortexUtils/docs/working-with-sdrs.md @@ -1,27 +1,27 @@ # Implement SDR representation samples ## Introduction: -Neural network is a focal element in the area of machine learning. Inspired from the biological neurons that are present in the human brain, an artificial neural network is designed which mimics human brain’s behavior, helping computer programs to identify patterns and answers to these related issues. It would be able to perform actions like the human brain and has the capability of learning things. These neural networks work on the principle of learning input/output operations. In our project, SDR representation has been implemented in a variety of ways, including SDR as indices and bitmaps. Furthermore, we developed methods for comparing two SDRs by using intersection, union, and overlap. In addition, we have added a new representation of Spatial pooler learning as a "Column/Overlap" ratio, which is a another representation as heatmap. +Neural network is a focal element in the area of machine learning. Inspired by the biological neurons that are present in the human brain, an artificial neural network is designed that mimics the human brain’s behavior, helping computer programs to identify patterns and answers to these related issues. It would be able to perform actions like the human brain and have the capability of learning things. These neural networks work on the principle of learning input/output operations. In our project, SDR representation has been implemented in a variety of ways, including SDR as indices and bitmaps. Furthermore, we developed methods for comparing two SDRs by using intersection, union, and overlap. In addition, we have added a new representation of Spatial pooler learning as a "Column/Overlap" ratio, which is another representation of a heatmap. -The inputs that we are using are scalar values and images. We specifed how these inputs are converted to SDR. Furthermore, this procedure of SDR representations involves the use of Encoders, Spatial Pooler (SP) and Temporal Memory (TM). Encoders are the basic components used in this network, which takes human justifiable information as input data i.e. (image, scalar value) and changes it to machine readable format, binary array with n size. SP uses these encoded binary arrays from encoders as an input for generation of SDRs. +The inputs that we are using are scalar values and images. We specified how these inputs are converted to SDR. Furthermore, this procedure of SDR representations involves the use of Encoders, Spatial Pooler (SP), and Temporal Memory (TM). Encoders are the basic components used in this network, which takes human justifiable information as input data i.e. (image, scalar value), and changes it to machine-readable format, binary array with n size. SP uses these encoded binary arrays from encoders as input for the generation of SDRs. -TM is used to learn the sequence of these generated SDR’s which are given as an input from Spatial Pooler (SP). +TM is used to learn the sequence of these generated SDRs which are given as input from the Spatial Pooler (SP). #### What is an SDR: -According to recent research in neuroscience, our brain uses SDRs to process information. SDRs are the binary representation of data which is approximatly 2% of bits which are active. In SDRs, each bit has a meaning i.e. the active bits in the same places of two different vectors make them semantically similar. By comparing SDRs of different samples, the similarity between them can be estimated. For storing the SDRs, a list of indices of active bits are kept which saves a lot of space. +According to recent research in neuroscience, our brain uses SDRs to process information. SDRs are the binary representation of data which is approximately 2% of bits that are active. In SDRs, each bit has a meaning i.e. the active bits in the same places of two different vectors make them semantically similar. By comparing SDRs of different samples, the similarity between them can be estimated. For storing the SDRs, a list of indices of active bits is kept which saves a lot of space. #### Functions dealing with SDRs: -SDRs are generated by passing an input to the encoder. Input can be anything like a number, time/date or an image. The type of encoder for each input is different. For example if our input is date and time we have to use DateTime Encoder and if our input is a number we use scaler encoder to encode it and represent it in form of SDRs. -It is sometimes important to visualise the SDRs in different forms and make comparsions between them for a better understnding of the learning process. We are going to show you how to output SDRs as: +SDRs are generated by passing an input to the encoder. Input can be anything like a number, time/date, or an image. The type of encoder for each input is different. For example, if our input is date and time we have to use DateTime Encoder, and if our input is a number we use a scaler encoder to encode it and represent it in the form of SDRs. +It is sometimes important to visualize the SDRs in different forms and make comparisons between them for a better understanding of the learning process. We are going to show you how to output SDRs as: - Indices - Bitmaps - Heatmaps -Other than the representations, we will show you comparisons of different inputs by using overlap and instersection functions to see how different sdrs are similar or distinct semantically. +Other than the representations, we will show you comparisons of different inputs by using overlap and instersection functions to see how different SDRs are similar or distinct semantically. The functions which are used for the SDR representation are listed: @@ -32,7 +32,7 @@ The functions which are used for the SDR representation are listed: ## How to output SDR as Indices: -SDR's are in the form of 0's & 1's, we have to look up for 1's everytime in an SDR array. So for simplification we just find the index number where SDR is 1. +SDRs are in the form of 0's & 1's, we have to look up for 1's every time in an SDR array. So for simplification, we just find the index number where SDR is 1. SDR as Indices is the returning of the index number where SDRs bits are 1 of a binary array. If you like to visualise SDRs as indices, you can do it by using this function `NeoCortexApi.Helpers.StringifyVector` to get indices of the active (1) bits from the binary array. This function receives the encoded SDR and returns the index numbers of the SDR array where it is active. @@ -51,13 +51,13 @@ Code URL for the SDR Indices: https://github.com/UniversityOfAppliedSciencesFrankfurt/se-cloud-2020-2021/blob/39475d910dd1bdbedc5e83be8bc72eefdd1259c7/MyProject/ML5.8SDRsRepresentations/OutputSDRs/Program.cs#L62 -## How to output SDR as bitmap: +## How to output SDR as a bitmap: #### What is a Bitmap: -Bitmap is a type of file format which is used to store images. A bitmap is a spatially mapped array of bits i.e. a map of bits. For the purpose of representing SDRs as bitmaps, we first feed the output of encoders as inputs to the SP. #### Heatmap Function: +Bitmap is a type of file format which is used to store images. A bitmap is a spatially mapped array of bits i.e. a map of bits. To represent SDRs as bitmaps, we first feed the output of encoders as inputs to the SP. #### Heatmap Function: #### Bitmap Function: -You can visualise Bitmaps using the function `NeoCortexUtils.DrawBitmap()`. We will show you simply how it works. In the first step, you need to convert a one dimensional binary array into two-dimensional binary array using predefined function `ArrayUtils.Make2DArray` as it is required for the conversion into bitmpas. Bitmaps are then generated from the 2D Array with or without a Transpose function `ArrayUtils.Transpose()`. Transpose function is used just to set the orientation. You can skip this part if you like. +You can visualize Bitmaps using the function `NeoCortexUtils.DrawBitmap()`. We will show you simply how it works. In the first step, you need to convert a one-dimensional binary array into the two-dimensional binary array using the predefined function `ArrayUtils.Make2DArray` as it is required for the conversion into bitmaps. Bitmaps are then generated from the 2D Array with or without a Transpose function `ArrayUtils.Transpose()`. A transpose function is used just to set the orientation. You can skip this part if you like. The following is a code snippet for the functions mentioned above: @@ -68,9 +68,9 @@ twoDimenArray = ArrayUtils.Transpose(twoDimenArray); NeoCortexUtils.DrawBitmap(twoDimArray1, 1024, 1024, $"{outFolder}\\Overlap_{sdrs.Count}.png", Color.PaleGreen, Color.Red, text: $"Overlap.png"); ``` -Now we would like to explain what is going on here. The `Array` of first argument is a one dimensional binary array, the second argument `(int)Math.Sqrt(Array.Length)` outputs the value of rows and the third argument `(int)Math.Sqrt(Array.Length)` gives the column values. In order to set the orientation of the bitmap image, we additionally use `ArrayUtils.Transpose()` function. The `twoDimArray` is passed to the `NeoCortexUtils.DrawBitmap` function for the representation of SDR as a bitmap image. +Now we would like to explain what is going on here. The `Array` of the first argument is a one-dimensional binary array, the second argument `(int)Math.Sqrt(Array.Length)` outputs the value of rows, and the third argument `(int)Math.Sqrt(Array.Length)` gives the column values. To set the orientation of the bitmap image, we additionally use `ArrayUtils.Transpose()` function. The `twoDimArray` is passed to the `NeoCortexUtils.DrawBitmap` function for the representation of SDR as a bitmap image. -`twoDimArray1` is the SDR representation in binary form which is to be plotted. `1024 , 1024` shows the size of the final bitmap image. You can adjust it based on your requirements. `$"{outFolder}\\Overlap_{sdrs.Count}.png"` shows the path where output image is going to be saved. `Color.PaleGreen, Color.Red` here you can set the colors of the On and Off bits. In my case, PaleGreen is for Off bits and Red is for On bits. `text: $"Overlap.png"` is it the text which is going to be visible on the top left of the image. +`twoDimArray1` is the SDR representation in binary form which is to be plotted. `1024 , 1024` shows the size of the final bitmap image. You can adjust it based on your requirements. `$"{outFolder}\\Overlap_{sdrs.Count}.png"` shows the path where the output image is going to be saved. `Color.PaleGreen, Color.Red` here you can set the colors of the On and Off bits. In my case, PaleGreen is for Off bits and Red is for On bits. `text: $"Overlap.png"` is the text which is going to be visible on the top left of the image. @@ -85,17 +85,17 @@ The following images are the output of the function `NeoCortexUtils.DrawBitmap() [img4]: https://user-images.githubusercontent.com/59200478/113517520-e3d73980-9599-11eb-956d-c299a5b452f6.png [img5]: https://user-images.githubusercontent.com/59200478/113517529-efc2fb80-9599-11eb-9eb7-90bbc97d42d5.png -URL of SDR code as bitmap : +URL of SDR code as a bitmap : https://github.com/UniversityOfAppliedSciencesFrankfurt/se-cloud-2020-2021/blob/39475d910dd1bdbedc5e83be8bc72eefdd1259c7/MyProject/ML5.8SDRsRepresentations/OutputSDRs/Program.cs#L109 ## How to output SDR as Heatmap: #### What is a Heatmap: -Sometimes is useful to see the activation of all cells. Some of them might be very active but still didn't reach the activation threshold. In such cases, the heatmap (URL) can be used. Heatmap is a graphical representation of data which uses a color coding system for representing different activation thresholds. +Sometimes is useful to see the activation of all cells. Some of them might be very active but still didn't reach the activation threshold. In such cases, the heatmap (URL) can be used. A heatmap is a graphical representation of data that uses a color coding system for representing different activation thresholds. #### Heatmap Function: -If you are looking to create a heatmap of an SDR learning. you can use `DrawHeatmaps` function but with some minor changes in the arguments. A heatmap representation of SDRs will depict the active columns and categorize them in different colors based on their usage during learning. How we are using the `DrawHeatmaps` function for this purpose you can see the arguments we have used to generate heatmaps down below. A code snippet is shown for the creation of heatmaps: +If you are looking to create a heatmap of an SDR learning. you can use the `DrawHeatmaps` function but with some minor changes in the arguments. A heatmap representation of SDRs will depict the active columns and categorize them in different colors based on their usage during learning. How we are using the `DrawHeatmaps` function for this purpose you can see the arguments we have used to generate heatmaps down below. A code snippet is shown for the creation of heatmaps: ```csharp public static void DrawHeatmaps(List twoDimArrays, String filePath, @@ -104,7 +104,7 @@ If you are looking to create a heatmap of an SDR learning. you can use `DrawHeat decimal redStart = 200, decimal yellowMiddle = 127, decimal greenStart = 20) ``` -Now here `decimal redStart = 200, decimal yellowMiddle = 127, decimal greenStart = 20` you can set the activation threshold of any cell with its color. In my case, red color is for the cell which are activated more than or equal to 200 times. Yellow color is for the cells which are activated more than or equal to 127 times. And green cells are those who are activated for 20 time or more. +Now here `decimal redStart = 200, decimal yellowMiddle = 127, decimal greenStart = 20` you can set the activation threshold of any cell with its color. In my case, the red color is for the cells that are activated more than or equal to 200 times. The yellow color is for the cells that are activated more than or equal to 127 times. Green cells are those that are activated 20 times or more. For the representation of SDRs as heatmaps, we took the following images which are the SDRs generated by encoders: From d2bee017581d9bba3c8a296a59e44f3baf22f42e Mon Sep 17 00:00:00 2001 From: Mahdieh Pirmoradian <74245613+MahdiehPirmoradian@users.noreply.github.com> Date: Sun, 14 Jan 2024 06:06:47 +0100 Subject: [PATCH 02/16] Update working-with-sdrs.md Title editing --- source/NeoCortexUtils/docs/working-with-sdrs.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/source/NeoCortexUtils/docs/working-with-sdrs.md b/source/NeoCortexUtils/docs/working-with-sdrs.md index a8c73cb36..2fa40a7d4 100644 --- a/source/NeoCortexUtils/docs/working-with-sdrs.md +++ b/source/NeoCortexUtils/docs/working-with-sdrs.md @@ -1,4 +1,4 @@ -# Implement SDR representation samples +# working-with-sdrs ## Introduction: Neural network is a focal element in the area of machine learning. Inspired by the biological neurons that are present in the human brain, an artificial neural network is designed that mimics the human brain’s behavior, helping computer programs to identify patterns and answers to these related issues. It would be able to perform actions like the human brain and have the capability of learning things. These neural networks work on the principle of learning input/output operations. In our project, SDR representation has been implemented in a variety of ways, including SDR as indices and bitmaps. Furthermore, we developed methods for comparing two SDRs by using intersection, union, and overlap. In addition, we have added a new representation of Spatial pooler learning as a "Column/Overlap" ratio, which is another representation of a heatmap. From acb954c45c659f3ed8227617e1b68892939834f9 Mon Sep 17 00:00:00 2001 From: Mahdieh Pirmoradian <74245613+MahdiehPirmoradian@users.noreply.github.com> Date: Sun, 14 Jan 2024 06:08:16 +0100 Subject: [PATCH 03/16] Update working-with-sdrs.md --- source/NeoCortexUtils/docs/working-with-sdrs.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/source/NeoCortexUtils/docs/working-with-sdrs.md b/source/NeoCortexUtils/docs/working-with-sdrs.md index 2fa40a7d4..e01235f47 100644 --- a/source/NeoCortexUtils/docs/working-with-sdrs.md +++ b/source/NeoCortexUtils/docs/working-with-sdrs.md @@ -1,7 +1,7 @@ # working-with-sdrs ## Introduction: -Neural network is a focal element in the area of machine learning. Inspired by the biological neurons that are present in the human brain, an artificial neural network is designed that mimics the human brain’s behavior, helping computer programs to identify patterns and answers to these related issues. It would be able to perform actions like the human brain and have the capability of learning things. These neural networks work on the principle of learning input/output operations. In our project, SDR representation has been implemented in a variety of ways, including SDR as indices and bitmaps. Furthermore, we developed methods for comparing two SDRs by using intersection, union, and overlap. In addition, we have added a new representation of Spatial pooler learning as a "Column/Overlap" ratio, which is another representation of a heatmap. +Neural network is a focal element in the area of machine learning. Inspired by the biological neurons that are present in the human brain, an artificial neural network is designed that mimics the human brain’s behavior, helping computer programs to identify patterns and answers to these related issues. It would be able to perform actions like the human brain and have the capability of learning things. These neural networks work on the principle of learning input/output operations. In this project, SDR representation has been implemented in a variety of ways, including SDR as indices and bitmaps. Furthermore, we developed methods for comparing two SDRs by using intersection, union, and overlap. In addition, we have added a new representation of Spatial pooler learning as a "Column/Overlap" ratio, which is another representation of a heatmap. The inputs that we are using are scalar values and images. We specified how these inputs are converted to SDR. Furthermore, this procedure of SDR representations involves the use of Encoders, Spatial Pooler (SP), and Temporal Memory (TM). Encoders are the basic components used in this network, which takes human justifiable information as input data i.e. (image, scalar value), and changes it to machine-readable format, binary array with n size. SP uses these encoded binary arrays from encoders as input for the generation of SDRs. From bc19444c50b313f76a427c5ade6e5492e99403fe Mon Sep 17 00:00:00 2001 From: Mahdieh Pirmoradian Date: Sun, 14 Jan 2024 18:06:45 +0100 Subject: [PATCH 04/16] upgrading the .Net version to 7 --- source/GridCell/GridCell.csproj | 63 +- source/HtmActorHost/HtmActorHost.csproj | 27 +- source/ImageEncoder/HtmImageEncoder.csproj | 69 +- .../NeoCortexApi.Experiments.csproj | 11 +- source/NeoCortexUtils/NeoCortexUtils.csproj | 11 +- .../UnitTestsProject/UnitTestsProject.csproj | 7599 ++++++++--------- 6 files changed, 3862 insertions(+), 3918 deletions(-) diff --git a/source/GridCell/GridCell.csproj b/source/GridCell/GridCell.csproj index 68ebe13dd..6a02c967d 100644 --- a/source/GridCell/GridCell.csproj +++ b/source/GridCell/GridCell.csproj @@ -1,34 +1,31 @@ - - - net5.0 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + net7.0 + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/source/HtmActorHost/HtmActorHost.csproj b/source/HtmActorHost/HtmActorHost.csproj index 3c5fb5e45..1381e993e 100644 --- a/source/HtmActorHost/HtmActorHost.csproj +++ b/source/HtmActorHost/HtmActorHost.csproj @@ -1,50 +1,39 @@  - Exe - net6.0 + net7.0 Linux - full true - full true - - - - - - - - - - - + + + + + + - - PreserveNewest - - + \ No newline at end of file diff --git a/source/ImageEncoder/HtmImageEncoder.csproj b/source/ImageEncoder/HtmImageEncoder.csproj index 75403ed68..31cdb8378 100644 --- a/source/ImageEncoder/HtmImageEncoder.csproj +++ b/source/ImageEncoder/HtmImageEncoder.csproj @@ -1,42 +1,29 @@  - - - net5.0 - enable - license.txt - true - - True - - HtmImageEncoder - - 1.0.1 - - Toan Thanh Truong, Damir Dobric - - HtmImageEncoder - - Package that extends NeocortexApi to work with images through ImageEncoder. It encodes the image to to the SDR. - - https://github.com/ddobric/neocortexapi/tree/ImageBinarizerEncoder/source/ImageEncoder - - https://github.com/ddobric/neocortexapi/tree/ImageBinarizerEncoder - - 1.0.1 - - 1.0.1 - - - - - True - - - - - - - - - - + + net7.0 + enable + license.txt + true + True + HtmImageEncoder + 1.0.1 + Toan Thanh Truong, Damir Dobric + HtmImageEncoder + Package that extends NeocortexApi to work with images through ImageEncoder. It encodes the image to to the SDR. + https://github.com/ddobric/neocortexapi/tree/ImageBinarizerEncoder/source/ImageEncoder + https://github.com/ddobric/neocortexapi/tree/ImageBinarizerEncoder + 1.0.1 + 1.0.1 + + + + True + + + + + + + + + \ No newline at end of file diff --git a/source/NeoCortexApi.Experiments/NeoCortexApi.Experiments.csproj b/source/NeoCortexApi.Experiments/NeoCortexApi.Experiments.csproj index 2bb3f5af0..d00759876 100644 --- a/source/NeoCortexApi.Experiments/NeoCortexApi.Experiments.csproj +++ b/source/NeoCortexApi.Experiments/NeoCortexApi.Experiments.csproj @@ -1,20 +1,15 @@ - - net5.0 - + net7.0 false - - - @@ -23,7 +18,6 @@ - PreserveNewest @@ -44,5 +38,4 @@ PreserveNewest - - + \ No newline at end of file diff --git a/source/NeoCortexUtils/NeoCortexUtils.csproj b/source/NeoCortexUtils/NeoCortexUtils.csproj index 0d453c4bc..4b224f01e 100644 --- a/source/NeoCortexUtils/NeoCortexUtils.csproj +++ b/source/NeoCortexUtils/NeoCortexUtils.csproj @@ -1,24 +1,19 @@  - - net5.0 + net7.0 - C:\dev\NeoCortexAPI\neocortexapi\source\NeoCortexUtils\NeoCortexUtils.xml full true - full true - - + - - + \ No newline at end of file diff --git a/source/UnitTestsProject/UnitTestsProject.csproj b/source/UnitTestsProject/UnitTestsProject.csproj index 34cdff53d..175e52c17 100644 --- a/source/UnitTestsProject/UnitTestsProject.csproj +++ b/source/UnitTestsProject/UnitTestsProject.csproj @@ -1,22 +1,17 @@  - - - net6.0 - - false - - - - TRACE;USE_AKKA - full - true - - - - full - true - - + + net7.0 + false + + + TRACE;USE_AKKA + full + true + + + full + true + @@ -28,3793 +23,3781 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - PreserveNewest - - - PreserveNewest - - - PreserveNewest - - - PreserveNewest - - - PreserveNewest - - - PreserveNewest - - - PreserveNewest - - - PreserveNewest - - - PreserveNewest - - - PreserveNewest - - - PreserveNewest - - - PreserveNewest - - - PreserveNewest - - - PreserveNewest - - - 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PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + PreserveNewest + + + + + + + + + + + + + \ No newline at end of file From cb036aee5fac05118200949ca488b77575a04d13 Mon Sep 17 00:00:00 2001 From: Mahdieh Pirmoradian Date: Sun, 14 Jan 2024 18:26:27 +0100 Subject: [PATCH 05/16] Upgrading the .Net Version to .Net 7.0 --- .../SequenceLearningExperiment.csproj | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/source/SequenceLearningExperiment/SequenceLearningExperiment.csproj b/source/SequenceLearningExperiment/SequenceLearningExperiment.csproj index c5fe0bb1e..84bccdcff 100644 --- a/source/SequenceLearningExperiment/SequenceLearningExperiment.csproj +++ b/source/SequenceLearningExperiment/SequenceLearningExperiment.csproj @@ -1,24 +1,19 @@ - Exe - net5.0 + net7.0 - full true - full true - - - + \ No newline at end of file From 0da0c20e8dc4e48ccc25c0b3d351843794cc398c Mon Sep 17 00:00:00 2001 From: Mahdieh Pirmoradian Date: Sun, 14 Jan 2024 18:28:49 +0100 Subject: [PATCH 06/16] Upgrading the .Net Version to .Net 7.0 --- .../ImageEncoderTest/ImageEncoderTest.csproj | 10 ++-------- 1 file changed, 2 insertions(+), 8 deletions(-) diff --git a/source/UnitTestsProject/ImageEncoderTest/ImageEncoderTest.csproj b/source/UnitTestsProject/ImageEncoderTest/ImageEncoderTest.csproj index 610684b44..972393e97 100644 --- a/source/UnitTestsProject/ImageEncoderTest/ImageEncoderTest.csproj +++ b/source/UnitTestsProject/ImageEncoderTest/ImageEncoderTest.csproj @@ -1,23 +1,18 @@  - - net6.0 + net7.0 enable - false - - - PreserveNewest @@ -32,5 +27,4 @@ PreserveNewest - - + \ No newline at end of file From ced40da5a5eed46410031952d73bb457a70c0c8e Mon Sep 17 00:00:00 2001 From: Damir Dobric Date: Mon, 15 Jan 2024 15:14:42 +0100 Subject: [PATCH 07/16] working on completion engine sample --- source/NeoCortexApi.sln | 7 ++ source/NeoCortexApi/SpatialPooler.cs | 7 +- .../Entities/TokenTable.cs | 12 +++ .../Interfaces/ICorpusLoader.cs | 19 +++++ .../Interfaces/ITokenSimilarity.cs | 23 ++++++ .../LevensteinSimilarity.cs | 43 +++++++++++ .../NeocortexApiLLMSample.csproj | 10 +++ source/NeocortexApiLLMSample/Program.cs | 16 ++++ .../TextFileCorpusLoader.cs | 73 +++++++++++++++++++ .../SpatialPatternLearning.cs | 2 +- source/UnitTestsProject/HtmClassifierTests.cs | 2 + 11 files changed, 207 insertions(+), 7 deletions(-) create mode 100644 source/NeocortexApiLLMSample/Entities/TokenTable.cs create mode 100644 source/NeocortexApiLLMSample/Interfaces/ICorpusLoader.cs create mode 100644 source/NeocortexApiLLMSample/Interfaces/ITokenSimilarity.cs create mode 100644 source/NeocortexApiLLMSample/LevensteinSimilarity.cs create mode 100644 source/NeocortexApiLLMSample/NeocortexApiLLMSample.csproj create mode 100644 source/NeocortexApiLLMSample/Program.cs create mode 100644 source/NeocortexApiLLMSample/TextFileCorpusLoader.cs diff --git a/source/NeoCortexApi.sln b/source/NeoCortexApi.sln index fd63cb329..8ef2ab0de 100644 --- a/source/NeoCortexApi.sln +++ b/source/NeoCortexApi.sln @@ -55,6 +55,8 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "GridCell", "GridCell\GridCe EndProject Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "NeoCortexApiAnomaly", "Samples\NeoCortexApiAnomaly\NeoCortexApiAnomaly.csproj", "{7F272910-3A59-4BBB-8888-9A7F695CA754}" EndProject +Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "NeocortexApiLLMSample", "NeocortexApiLLMSample\NeocortexApiLLMSample.csproj", "{8D1F778F-AF73-44CF-965E-C061C07B99A7}" +EndProject Global GlobalSection(SolutionConfigurationPlatforms) = preSolution Debug|Any CPU = Debug|Any CPU @@ -117,6 +119,10 @@ Global {7F272910-3A59-4BBB-8888-9A7F695CA754}.Debug|Any CPU.Build.0 = Debug|Any CPU {7F272910-3A59-4BBB-8888-9A7F695CA754}.Release|Any CPU.ActiveCfg = Release|Any CPU {7F272910-3A59-4BBB-8888-9A7F695CA754}.Release|Any CPU.Build.0 = Release|Any CPU + {8D1F778F-AF73-44CF-965E-C061C07B99A7}.Debug|Any CPU.ActiveCfg = Debug|Any CPU + {8D1F778F-AF73-44CF-965E-C061C07B99A7}.Debug|Any CPU.Build.0 = Debug|Any CPU + {8D1F778F-AF73-44CF-965E-C061C07B99A7}.Release|Any CPU.ActiveCfg = Release|Any CPU + {8D1F778F-AF73-44CF-965E-C061C07B99A7}.Release|Any CPU.Build.0 = Release|Any CPU EndGlobalSection GlobalSection(SolutionProperties) = preSolution HideSolutionNode = FALSE @@ -126,6 +132,7 @@ Global {15375C28-1424-4A29-B2AF-11ED92F6134C} = {99AE2313-7D07-4905-A0BB-52D304FCC2F7} {AE2CF42C-DC7D-4F0A-8B43-84A5D81E1D72} = {99AE2313-7D07-4905-A0BB-52D304FCC2F7} {7F272910-3A59-4BBB-8888-9A7F695CA754} = {99AE2313-7D07-4905-A0BB-52D304FCC2F7} + {8D1F778F-AF73-44CF-965E-C061C07B99A7} = {99AE2313-7D07-4905-A0BB-52D304FCC2F7} EndGlobalSection GlobalSection(ExtensibilityGlobals) = postSolution SolutionGuid = {71185BAD-1342-4836-BCDA-6EC97118F92D} diff --git a/source/NeoCortexApi/SpatialPooler.cs b/source/NeoCortexApi/SpatialPooler.cs index 5b9e59a23..b439dc033 100644 --- a/source/NeoCortexApi/SpatialPooler.cs +++ b/source/NeoCortexApi/SpatialPooler.cs @@ -131,13 +131,8 @@ public virtual void InitMatrices(Connections conn, DistributedMemory distMem) colList.Add(new KeyPair() { Key = i, Value = new Column(numCells, i, conn.HtmConfig.SynPermConnected, conn.HtmConfig.NumInputs) }); } - Stopwatch sw = new Stopwatch(); - sw.Start(); - conn.Memory.set(colList); - - sw.Stop(); - + //Initialize state meta-management statistics conn.HtmConfig.OverlapDutyCycles = new double[numColumns]; conn.HtmConfig.ActiveDutyCycles = new double[numColumns]; diff --git a/source/NeocortexApiLLMSample/Entities/TokenTable.cs b/source/NeocortexApiLLMSample/Entities/TokenTable.cs new file mode 100644 index 000000000..37bd8dbb9 --- /dev/null +++ b/source/NeocortexApiLLMSample/Entities/TokenTable.cs @@ -0,0 +1,12 @@ +using System; +using System.Collections.Generic; +using System.Linq; +using System.Text; +using System.Threading.Tasks; + +namespace NeocortexApiLLMSample.Entities +{ + public class TokenTable + { + } +} diff --git a/source/NeocortexApiLLMSample/Interfaces/ICorpusLoader.cs b/source/NeocortexApiLLMSample/Interfaces/ICorpusLoader.cs new file mode 100644 index 000000000..006db83b9 --- /dev/null +++ b/source/NeocortexApiLLMSample/Interfaces/ICorpusLoader.cs @@ -0,0 +1,19 @@ +using System; +using System.Collections.Generic; +using System.Linq; +using System.Text; +using System.Threading.Tasks; + +namespace NeocortexApiLLMSample.Interfaces +{ + public interface ICorpusLoader + { + /// + /// Loads the corpus data from the given bookmark. + /// + /// Specifies ehere to start loading. + /// Return the bookmark, that might be used at the next call. + Task Load(string bookmark); + + } +} diff --git a/source/NeocortexApiLLMSample/Interfaces/ITokenSimilarity.cs b/source/NeocortexApiLLMSample/Interfaces/ITokenSimilarity.cs new file mode 100644 index 000000000..6f80fc746 --- /dev/null +++ b/source/NeocortexApiLLMSample/Interfaces/ITokenSimilarity.cs @@ -0,0 +1,23 @@ +using System; +using System.Collections.Generic; +using System.Linq; +using System.Text; +using System.Threading.Tasks; + +namespace NeocortexApiLLMSample.Interfaces +{ + /// + /// Defines the used to calculate similarity between tokens. + /// + public interface ITokenSimilarity + { + /// + /// Calculates the similarity between two words. It does not use embeddings or similar technique. + /// + /// Any kind of token like syllable. + /// + /// + int CalcSimilarity(string token1, string token2); + + } +} diff --git a/source/NeocortexApiLLMSample/LevensteinSimilarity.cs b/source/NeocortexApiLLMSample/LevensteinSimilarity.cs new file mode 100644 index 000000000..6d04ea987 --- /dev/null +++ b/source/NeocortexApiLLMSample/LevensteinSimilarity.cs @@ -0,0 +1,43 @@ +using NeocortexApiLLMSample.Interfaces; +using System; +using System.Collections.Generic; +using System.Linq; +using System.Text; +using System.Threading.Tasks; + +namespace NeocortexApiLLMSample +{ + internal class LevensteinSimilarity : ITokenSimilarity + { + public int CalcSimilarity(string word1, string word2) + { + int m = word1.Length; + int n = word2.Length; + + // Create a 2D matrix to store distances + int[,] dp = new int[m + 1, n + 1]; + + // Initialize the matrix + for (int i = 0; i <= m; ++i) + dp[i, 0] = i; + + for (int j = 0; j <= n; ++j) + dp[0, j] = j; + + // Fill the matrix + for (int i = 1; i <= m; ++i) + { + for (int j = 1; j <= n; ++j) + { + if (word1[i - 1] == word2[j - 1]) + dp[i, j] = dp[i - 1, j - 1]; + else + dp[i, j] = 1 + Math.Min(Math.Min(dp[i - 1, j], dp[i, j - 1]), dp[i - 1, j - 1]); + } + } + + return dp[m, n]; + } + } + +} diff --git a/source/NeocortexApiLLMSample/NeocortexApiLLMSample.csproj b/source/NeocortexApiLLMSample/NeocortexApiLLMSample.csproj new file mode 100644 index 000000000..2150e3797 --- /dev/null +++ b/source/NeocortexApiLLMSample/NeocortexApiLLMSample.csproj @@ -0,0 +1,10 @@ + + + + Exe + net8.0 + enable + enable + + + diff --git a/source/NeocortexApiLLMSample/Program.cs b/source/NeocortexApiLLMSample/Program.cs new file mode 100644 index 000000000..8936f797f --- /dev/null +++ b/source/NeocortexApiLLMSample/Program.cs @@ -0,0 +1,16 @@ +using NeocortexApiLLMSample.Interfaces; + +namespace NeocortexApiLLMSample +{ + internal class Program + { + static async Task Main(string[] args) + { + Console.WriteLine("Hello, LLM!"); + + ICorpusLoader loader = new TextFileCorpusLoader("corpus.txt"); + + await loader.Load("bookmark"); + } + } +} diff --git a/source/NeocortexApiLLMSample/TextFileCorpusLoader.cs b/source/NeocortexApiLLMSample/TextFileCorpusLoader.cs new file mode 100644 index 000000000..570674eb4 --- /dev/null +++ b/source/NeocortexApiLLMSample/TextFileCorpusLoader.cs @@ -0,0 +1,73 @@ +using System; +using System.Collections.Generic; +using System.Linq; +using System.Text; +using System.Text.RegularExpressions; +using System.Threading.Tasks; + +namespace NeocortexApiLLMSample +{ + public class TextFileCorpusLoader : Interfaces.ICorpusLoader + { + private readonly string _file; + + private Dictionary _words = new Dictionary(); + + public TextFileCorpusLoader(string fileName) + { + if (string.IsNullOrEmpty(fileName)) + { + throw new ArgumentException("File name must not be null or empty.", nameof(fileName)); + } + + _file = fileName; + } + + public async Task Load(string bookmark) + { + using (StreamReader sr = new StreamReader(_file)) + { + string line = await sr.ReadLineAsync()!; + + if (line != null) + { + var words = ExtractWords(line); + } + } + + // We do not support bookmaring right now. + return null; + } + + private static List ExtractWords(string input) + { + // Use a regular expression to match word boundaries + // \b asserts a word boundary + // \w+ matches one or more word characters (alphanumeric or underscore) + MatchCollection matches = Regex.Matches(input, @"\b\w+\b"); + + // Convert MatchCollection to a List + List words = new List(); + foreach (Match match in matches) + { + words.Add(match.Value); + } + + return words; + } + + public IEnumerator GetEnumerator() + { + using (StreamReader sr = new StreamReader(_file)) + { + string? line = sr.ReadLine(); + while (line != null) + { + yield return line; + line = sr.ReadLine(); + } + } + } + } + +} diff --git a/source/Samples/NeoCortexApiSample/SpatialPatternLearning.cs b/source/Samples/NeoCortexApiSample/SpatialPatternLearning.cs index d7e598136..b68d2af66 100644 --- a/source/Samples/NeoCortexApiSample/SpatialPatternLearning.cs +++ b/source/Samples/NeoCortexApiSample/SpatialPatternLearning.cs @@ -81,7 +81,7 @@ public void Run() var sp = RunExperiment(cfg, encoder, inputValues); - RunRustructuringExperiment(sp, encoder, inputValues); + //RunRustructuringExperiment(sp, encoder, inputValues); } diff --git a/source/UnitTestsProject/HtmClassifierTests.cs b/source/UnitTestsProject/HtmClassifierTests.cs index 958be09c9..aa0e12c04 100644 --- a/source/UnitTestsProject/HtmClassifierTests.cs +++ b/source/UnitTestsProject/HtmClassifierTests.cs @@ -129,6 +129,8 @@ public void NoExceptionIfCellsCountIsZero() [TestMethod] public void CheckHowManyOfGetPredictedInputValues(int howMany) { + throw new NotImplementedException(); + var predictiveCells = getMockCells(CellActivity.PredictiveCell); var res = htmClassifier.GetPredictedInputValues(predictiveCells.ToArray(), Convert.ToInt16(howMany)); From 486be0bf838a900fff1df4602aa0450410582705 Mon Sep 17 00:00:00 2001 From: Damir Dobric Date: Mon, 15 Jan 2024 15:18:36 +0100 Subject: [PATCH 08/16] added an issue in UT --- source/UnitTestsProject/HtmClassifierTests.cs | 2 -- 1 file changed, 2 deletions(-) diff --git a/source/UnitTestsProject/HtmClassifierTests.cs b/source/UnitTestsProject/HtmClassifierTests.cs index aa0e12c04..958be09c9 100644 --- a/source/UnitTestsProject/HtmClassifierTests.cs +++ b/source/UnitTestsProject/HtmClassifierTests.cs @@ -129,8 +129,6 @@ public void NoExceptionIfCellsCountIsZero() [TestMethod] public void CheckHowManyOfGetPredictedInputValues(int howMany) { - throw new NotImplementedException(); - var predictiveCells = getMockCells(CellActivity.PredictiveCell); var res = htmClassifier.GetPredictedInputValues(predictiveCells.ToArray(), Convert.ToInt16(howMany)); From 39d506cadd9e114b12a2b3986d2e26a7cdce6494 Mon Sep 17 00:00:00 2001 From: Mahdieh Pirmoradian <74245613+MahdiehPirmoradian@users.noreply.github.com> Date: Mon, 15 Jan 2024 15:22:11 +0100 Subject: [PATCH 09/16] Update dotnet-core.yml Adiing .Net 8.0 to the test --- .github/workflows/dotnet-core.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/dotnet-core.yml b/.github/workflows/dotnet-core.yml index 351e39a60..4ab8244b8 100644 --- a/.github/workflows/dotnet-core.yml +++ b/.github/workflows/dotnet-core.yml @@ -24,6 +24,7 @@ jobs: 5.0.x 6.0.x 7.0.x + 8.0.x - name: Install dependencies run: dotnet restore ${{env.MainSln}} From 5d78e86ce4feda72b2b2b87ac29092fd99c1e9ef Mon Sep 17 00:00:00 2001 From: Mahdieh Pirmoradian Date: Mon, 15 Jan 2024 16:54:28 +0100 Subject: [PATCH 10/16] Upgrading the .Net Version to .Net 8.0 --- source/GridCell/GridCell.csproj | 2 +- source/HtmActorHost/HtmActorHost.csproj | 14 +++++++------- source/ImageEncoder/HtmImageEncoder.csproj | 2 +- .../NeoCortexApi.Experiments.csproj | 2 +- source/NeoCortexApi.sln | 4 ++-- source/NeoCortexUtils/NeoCortexUtils.csproj | 4 ++-- .../NeoCortexApiAnomaly/NeoCortexApiAnomaly.csproj | 7 ++----- .../NeoCortexApiPersistenceSample.csproj | 7 ++----- .../NeoCortexApiSample/NeoCortexApiSample.csproj | 13 ++++--------- source/UnitTestsProject/UnitTestsProject.csproj | 4 ++-- 10 files changed, 24 insertions(+), 35 deletions(-) diff --git a/source/GridCell/GridCell.csproj b/source/GridCell/GridCell.csproj index 6a02c967d..528adc24c 100644 --- a/source/GridCell/GridCell.csproj +++ b/source/GridCell/GridCell.csproj @@ -1,6 +1,6 @@ - net7.0 + net8.0 diff --git a/source/HtmActorHost/HtmActorHost.csproj b/source/HtmActorHost/HtmActorHost.csproj index 1381e993e..821cc23c9 100644 --- a/source/HtmActorHost/HtmActorHost.csproj +++ b/source/HtmActorHost/HtmActorHost.csproj @@ -1,7 +1,7 @@  Exe - net7.0 + net8.0 Linux @@ -20,12 +20,12 @@ - - - - - - + + + + + + diff --git a/source/ImageEncoder/HtmImageEncoder.csproj b/source/ImageEncoder/HtmImageEncoder.csproj index 31cdb8378..91ec8b259 100644 --- a/source/ImageEncoder/HtmImageEncoder.csproj +++ b/source/ImageEncoder/HtmImageEncoder.csproj @@ -1,6 +1,6 @@  - net7.0 + net8.0 enable license.txt true diff --git a/source/NeoCortexApi.Experiments/NeoCortexApi.Experiments.csproj b/source/NeoCortexApi.Experiments/NeoCortexApi.Experiments.csproj index d00759876..36c836b16 100644 --- a/source/NeoCortexApi.Experiments/NeoCortexApi.Experiments.csproj +++ b/source/NeoCortexApi.Experiments/NeoCortexApi.Experiments.csproj @@ -1,6 +1,6 @@ - net7.0 + net8.0 false diff --git a/source/NeoCortexApi.sln b/source/NeoCortexApi.sln index 8ef2ab0de..a1e4a135e 100644 --- a/source/NeoCortexApi.sln +++ b/source/NeoCortexApi.sln @@ -1,4 +1,4 @@ - + Microsoft Visual Studio Solution File, Format Version 12.00 # Visual Studio Version 17 VisualStudioVersion = 17.0.32014.148 @@ -53,7 +53,7 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "nuget", "nuget", "{9475B9AE EndProject Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "GridCell", "GridCell\GridCell.csproj", "{E102D57D-BA8F-4E21-8365-8ABAFB5D2C94}" EndProject -Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "NeoCortexApiAnomaly", "Samples\NeoCortexApiAnomaly\NeoCortexApiAnomaly.csproj", "{7F272910-3A59-4BBB-8888-9A7F695CA754}" +Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "NeoCortexApiAnomaly", "Samples\NeoCortexApiAnomaly\NeoCortexApiAnomaly.csproj", "{7F272910-3A59-4BBB-8888-9A7F695CA754}" EndProject Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "NeocortexApiLLMSample", "NeocortexApiLLMSample\NeocortexApiLLMSample.csproj", "{8D1F778F-AF73-44CF-965E-C061C07B99A7}" EndProject diff --git a/source/NeoCortexUtils/NeoCortexUtils.csproj b/source/NeoCortexUtils/NeoCortexUtils.csproj index 4b224f01e..34a46e081 100644 --- a/source/NeoCortexUtils/NeoCortexUtils.csproj +++ b/source/NeoCortexUtils/NeoCortexUtils.csproj @@ -1,6 +1,6 @@  - net7.0 + net8.0 C:\dev\NeoCortexAPI\neocortexapi\source\NeoCortexUtils\NeoCortexUtils.xml @@ -14,6 +14,6 @@ - + \ No newline at end of file diff --git a/source/Samples/NeoCortexApiAnomaly/NeoCortexApiAnomaly.csproj b/source/Samples/NeoCortexApiAnomaly/NeoCortexApiAnomaly.csproj index b0b6f2666..a725004a2 100644 --- a/source/Samples/NeoCortexApiAnomaly/NeoCortexApiAnomaly.csproj +++ b/source/Samples/NeoCortexApiAnomaly/NeoCortexApiAnomaly.csproj @@ -1,16 +1,13 @@  - Exe - net7.0 + net8.0 enable enable - - - + \ No newline at end of file diff --git a/source/Samples/NeoCortexApiPersistenceSample/NeoCortexApiPersistenceSample.csproj b/source/Samples/NeoCortexApiPersistenceSample/NeoCortexApiPersistenceSample.csproj index 404bb2508..8a8efcc0c 100644 --- a/source/Samples/NeoCortexApiPersistenceSample/NeoCortexApiPersistenceSample.csproj +++ b/source/Samples/NeoCortexApiPersistenceSample/NeoCortexApiPersistenceSample.csproj @@ -1,16 +1,13 @@ - Exe - net7.0 + net8.0 enable enable - - - + \ No newline at end of file diff --git a/source/Samples/NeoCortexApiSample/NeoCortexApiSample.csproj b/source/Samples/NeoCortexApiSample/NeoCortexApiSample.csproj index 49df32396..df5b34f8b 100644 --- a/source/Samples/NeoCortexApiSample/NeoCortexApiSample.csproj +++ b/source/Samples/NeoCortexApiSample/NeoCortexApiSample.csproj @@ -1,30 +1,25 @@  - Exe - net7.0 - + net8.0 + + - full true - full true - - - - + \ No newline at end of file diff --git a/source/UnitTestsProject/UnitTestsProject.csproj b/source/UnitTestsProject/UnitTestsProject.csproj index 175e52c17..be600208a 100644 --- a/source/UnitTestsProject/UnitTestsProject.csproj +++ b/source/UnitTestsProject/UnitTestsProject.csproj @@ -1,6 +1,6 @@  - net7.0 + net8.0 false @@ -46,7 +46,7 @@ - + From c6c7825bb98a3350f02b6b3ae4bbffc303b4f36a Mon Sep 17 00:00:00 2001 From: Singari Sahith Kumar Date: Sun, 21 Jan 2024 19:27:17 +0100 Subject: [PATCH 11/16] Approve Multisequence Learning --- source/NeoCortexApi.sln | 10 +- .../ApproveMultiSequenceLearning.csproj | 39 +++ .../DataSet/dataset_01.json | 1 + .../DataSet/dataset_02.json | 1 + .../DataSet/dataset_03.json | 1 + .../DataSet/dataset_04.json | 1 + .../DataSet/test_01.json | 18 + .../HelperMethods.cs | 280 +++++++++++++++ .../MultiSequenceLearning.csproj | 20 ++ .../MultisequenceLearning.cs | 321 ++++++++++++++++++ .../ApproveMultiSequenceLearning/Program.cs | 228 +++++++++++++ .../ApproveMultiSequenceLearning/Report.cs | 20 ++ .../ApproveMultiSequenceLearning/Sequence.cs | 14 + .../report/report_638172569568213902.txt | 33 ++ .../report/report_638172649322130486.txt | 60 ++++ 15 files changed, 1045 insertions(+), 2 deletions(-) create mode 100644 source/Samples/ApproveMultiSequenceLearning/ApproveMultiSequenceLearning.csproj create mode 100644 source/Samples/ApproveMultiSequenceLearning/DataSet/dataset_01.json create mode 100644 source/Samples/ApproveMultiSequenceLearning/DataSet/dataset_02.json create mode 100644 source/Samples/ApproveMultiSequenceLearning/DataSet/dataset_03.json create mode 100644 source/Samples/ApproveMultiSequenceLearning/DataSet/dataset_04.json create mode 100644 source/Samples/ApproveMultiSequenceLearning/DataSet/test_01.json create mode 100644 source/Samples/ApproveMultiSequenceLearning/HelperMethods.cs create mode 100644 source/Samples/ApproveMultiSequenceLearning/MultiSequenceLearning.csproj create mode 100644 source/Samples/ApproveMultiSequenceLearning/MultisequenceLearning.cs create mode 100644 source/Samples/ApproveMultiSequenceLearning/Program.cs create mode 100644 source/Samples/ApproveMultiSequenceLearning/Report.cs create mode 100644 source/Samples/ApproveMultiSequenceLearning/Sequence.cs create mode 100644 source/Samples/ApproveMultiSequenceLearning/report/report_638172569568213902.txt create mode 100644 source/Samples/ApproveMultiSequenceLearning/report/report_638172649322130486.txt diff --git a/source/NeoCortexApi.sln b/source/NeoCortexApi.sln index a1e4a135e..424f487d3 100644 --- a/source/NeoCortexApi.sln +++ b/source/NeoCortexApi.sln @@ -1,4 +1,3 @@ - Microsoft Visual Studio Solution File, Format Version 12.00 # Visual Studio Version 17 VisualStudioVersion = 17.0.32014.148 @@ -55,7 +54,9 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "GridCell", "GridCell\GridCe EndProject Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "NeoCortexApiAnomaly", "Samples\NeoCortexApiAnomaly\NeoCortexApiAnomaly.csproj", "{7F272910-3A59-4BBB-8888-9A7F695CA754}" EndProject -Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "NeocortexApiLLMSample", "NeocortexApiLLMSample\NeocortexApiLLMSample.csproj", "{8D1F778F-AF73-44CF-965E-C061C07B99A7}" +Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "NeocortexApiLLMSample", "NeocortexApiLLMSample\NeocortexApiLLMSample.csproj", "{8D1F778F-AF73-44CF-965E-C061C07B99A7}" +EndProject +Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "ApproveMultiSequenceLearning", "Samples\ApproveMultiSequenceLearning\ApproveMultiSequenceLearning.csproj", "{D56D5B70-0094-41F0-9FFD-BCAB50BA7737}" EndProject Global GlobalSection(SolutionConfigurationPlatforms) = preSolution @@ -123,6 +124,10 @@ Global {8D1F778F-AF73-44CF-965E-C061C07B99A7}.Debug|Any CPU.Build.0 = Debug|Any CPU {8D1F778F-AF73-44CF-965E-C061C07B99A7}.Release|Any CPU.ActiveCfg = Release|Any CPU {8D1F778F-AF73-44CF-965E-C061C07B99A7}.Release|Any CPU.Build.0 = Release|Any CPU + {D56D5B70-0094-41F0-9FFD-BCAB50BA7737}.Debug|Any CPU.ActiveCfg = Debug|Any CPU + {D56D5B70-0094-41F0-9FFD-BCAB50BA7737}.Debug|Any CPU.Build.0 = Debug|Any CPU + {D56D5B70-0094-41F0-9FFD-BCAB50BA7737}.Release|Any CPU.ActiveCfg = Release|Any CPU + {D56D5B70-0094-41F0-9FFD-BCAB50BA7737}.Release|Any CPU.Build.0 = Release|Any CPU EndGlobalSection GlobalSection(SolutionProperties) = preSolution HideSolutionNode = FALSE @@ -133,6 +138,7 @@ Global {AE2CF42C-DC7D-4F0A-8B43-84A5D81E1D72} = {99AE2313-7D07-4905-A0BB-52D304FCC2F7} {7F272910-3A59-4BBB-8888-9A7F695CA754} = {99AE2313-7D07-4905-A0BB-52D304FCC2F7} {8D1F778F-AF73-44CF-965E-C061C07B99A7} = {99AE2313-7D07-4905-A0BB-52D304FCC2F7} + {D56D5B70-0094-41F0-9FFD-BCAB50BA7737} = {99AE2313-7D07-4905-A0BB-52D304FCC2F7} EndGlobalSection GlobalSection(ExtensibilityGlobals) = postSolution SolutionGuid = {71185BAD-1342-4836-BCDA-6EC97118F92D} diff --git a/source/Samples/ApproveMultiSequenceLearning/ApproveMultiSequenceLearning.csproj b/source/Samples/ApproveMultiSequenceLearning/ApproveMultiSequenceLearning.csproj new file mode 100644 index 000000000..9835eb800 --- /dev/null +++ b/source/Samples/ApproveMultiSequenceLearning/ApproveMultiSequenceLearning.csproj @@ -0,0 +1,39 @@ + + + + Exe + net8.0 + enable + enable + + + + + + + + + + + + + + + + Always + + + Always + + + Always + + + Always + + + Always + + + + diff --git a/source/Samples/ApproveMultiSequenceLearning/DataSet/dataset_01.json b/source/Samples/ApproveMultiSequenceLearning/DataSet/dataset_01.json new file mode 100644 index 000000000..fd4a66239 --- /dev/null +++ b/source/Samples/ApproveMultiSequenceLearning/DataSet/dataset_01.json @@ -0,0 +1 @@ +[{"name":"S1","data":[0,2,5,7,8,11,13,14,17,21,23,24,25,26,27,28,29]},{"name":"S2","data":[0,1,2,3,4,6,7,9,14,15,17,22,23,24,25,27,28]},{"name":"S3","data":[2,3,5,9,10,11,12,13,17,19,20,21,23,25,26,27,29]}] diff --git a/source/Samples/ApproveMultiSequenceLearning/DataSet/dataset_02.json b/source/Samples/ApproveMultiSequenceLearning/DataSet/dataset_02.json new file mode 100644 index 000000000..c694ac433 --- /dev/null +++ b/source/Samples/ApproveMultiSequenceLearning/DataSet/dataset_02.json @@ -0,0 +1 @@ +[{"name":"S1","data":[0,2,4,5,6,7,8,9,11,13,14,15,16,19]},{"name":"S2","data":[0,1,2,4,5,10,12,13,14,15,16,17,18,19]},{"name":"S3","data":[0,1,3,4,6,7,8,10,11,13,16,17,18,19]}] diff --git a/source/Samples/ApproveMultiSequenceLearning/DataSet/dataset_03.json b/source/Samples/ApproveMultiSequenceLearning/DataSet/dataset_03.json new file mode 100644 index 000000000..6698e3ffe --- /dev/null +++ b/source/Samples/ApproveMultiSequenceLearning/DataSet/dataset_03.json @@ -0,0 +1 @@ +[{"name":"S1","data":[0,1,4,5,6,7,8,13,14]},{"name":"S2","data":[0,1,2,3,4,5,7,8,11]},{"name":"S3","data":[0,1,2,3,4,5,6,9,13]},{"name":"S4","data":[1,3,5,8,9,10,11,12,14]},{"name":"S5","data":[0,3,4,6,7,10,12,13,14]},{"name":"S6","data":[2,3,4,5,7,8,9,10,14]},{"name":"S7","data":[0,2,3,4,5,6,8,10,13]},{"name":"S8","data":[1,2,4,5,6,10,11,12,13]},{"name":"S9","data":[0,1,2,3,4,8,10,12,13]},{"name":"S10","data":[0,1,2,4,5,6,7,9,11]},{"name":"S11","data":[2,3,4,9,10,11,12,13,14]},{"name":"S12","data":[0,2,3,4,5,6,7,12,13]},{"name":"S13","data":[1,2,4,6,8,9,11,12,14]},{"name":"S14","data":[2,4,6,8,9,10,11,12,13]},{"name":"S15","data":[0,1,3,6,8,9,10,13,14]},{"name":"S16","data":[1,2,3,8,9,10,11,12,14]},{"name":"S17","data":[0,1,3,4,6,7,9,10,13]},{"name":"S18","data":[1,3,5,6,8,9,10,12,13]},{"name":"S19","data":[1,2,4,5,6,10,11,12,14]},{"name":"S20","data":[0,2,4,5,6,9,10,13,14]},{"name":"S21","data":[0,4,5,6,7,8,11,13,14]},{"name":"S22","data":[0,3,5,6,7,8,9,10,13]},{"name":"S23","data":[0,1,2,3,5,7,9,10,13]},{"name":"S24","data":[0,3,4,6,8,9,10,13,14]},{"name":"S25","data":[0,1,2,3,5,8,10,12,13]},{"name":"S26","data":[0,3,5,6,8,9,10,12,13]},{"name":"S27","data":[1,3,4,5,6,8,11,12,14]},{"name":"S28","data":[0,3,5,6,7,9,11,12,13]},{"name":"S29","data":[0,2,3,4,5,6,11,12,14]},{"name":"S30","data":[0,1,2,3,4,6,8,9,12]}] diff --git a/source/Samples/ApproveMultiSequenceLearning/DataSet/dataset_04.json b/source/Samples/ApproveMultiSequenceLearning/DataSet/dataset_04.json new file mode 100644 index 000000000..9ec97ca11 --- /dev/null +++ b/source/Samples/ApproveMultiSequenceLearning/DataSet/dataset_04.json @@ -0,0 +1 @@ +[{"name":"S1","data":[0,2,5,6,7,8,10,11,13]},{"name":"S2","data":[1,2,3,4,6,11,12,13,14]},{"name":"S3","data":[1,2,3,4,7,8,10,12,14]}] diff --git a/source/Samples/ApproveMultiSequenceLearning/DataSet/test_01.json b/source/Samples/ApproveMultiSequenceLearning/DataSet/test_01.json new file mode 100644 index 000000000..3785c6d06 --- /dev/null +++ b/source/Samples/ApproveMultiSequenceLearning/DataSet/test_01.json @@ -0,0 +1,18 @@ +[ + { + "name": "T1", + "data": [ 5, 6, 7, 8 ] + }, + { + "name": "T2", + "data": [ 6, 11, 12, 13 ] + }, + { + "name": "T3", + "data": [ 1, 2, 3, 4 ] + }, + { + "name": "T4", + "data": [ 3, 4, 7, 8, 10 ] + } +] diff --git a/source/Samples/ApproveMultiSequenceLearning/HelperMethods.cs b/source/Samples/ApproveMultiSequenceLearning/HelperMethods.cs new file mode 100644 index 000000000..84225d7f8 --- /dev/null +++ b/source/Samples/ApproveMultiSequenceLearning/HelperMethods.cs @@ -0,0 +1,280 @@ +using System; +using System.Globalization; +using System.IO; +using System.Collections.Generic; +using System.Linq; +using System.Text; +using System.Threading.Tasks; +using NeoCortexApi; +using NeoCortexApi.Encoders; +using NeoCortexApi.Entities; +using MultiSequenceLearning; +using Newtonsoft.Json; + +namespace MultiSequenceLearning +{ + public class HelperMethods + { + public HelperMethods() + { + //needs no implementation + } + + /// + /// HTM Config for creating Connections + /// + /// input bits + /// number of columns + /// Object of HTMConfig + public static HtmConfig FetchHTMConfig(int inputBits, int numColumns) + { + HtmConfig cfg = new HtmConfig(new int[] { inputBits }, new int[] { numColumns }) + { + Random = new ThreadSafeRandom(42), + + CellsPerColumn = 25, + GlobalInhibition = true, + LocalAreaDensity = -1, + NumActiveColumnsPerInhArea = 0.02 * numColumns, + PotentialRadius = (int)(0.15 * inputBits), + //InhibitionRadius = 15, + + MaxBoost = 10.0, + DutyCyclePeriod = 25, + MinPctOverlapDutyCycles = 0.75, + MaxSynapsesPerSegment = (int)(0.02 * numColumns), + + ActivationThreshold = 15, + ConnectedPermanence = 0.5, + + // Learning is slower than forgetting in this case. + PermanenceDecrement = 0.25, + PermanenceIncrement = 0.15, + + // Used by punishing of segments. + PredictedSegmentDecrement = 0.1, + + //NumInputs = 88 + }; + + return cfg; + } + + /// + /// Takes in user input and return encoded SDR for prediction + /// + /// + /// + public static int[] EncodeSingleInput(string userInput) + { + int[] sdr = new int[0]; + + //needs no implementation + + return sdr; + } + + /// + /// Get the encoder with settings + /// + /// input bits + /// Object of EncoderBase + public static EncoderBase GetEncoder(int inputBits) + { + double max = 20; + + Dictionary settings = new Dictionary() + { + { "W", 15}, + { "N", inputBits}, + { "Radius", -1.0}, + { "MinVal", 0.0}, + { "Periodic", false}, + { "Name", "scalar"}, + { "ClipInput", false}, + { "MaxVal", max} + }; + + EncoderBase encoder = new ScalarEncoder(settings); + + return encoder; + } + + /// + /// Reads dataset from the file + /// + /// full path of the file + /// Object of list of Sequence + public static List ReadDataset(string path) + { + Console.WriteLine("Reading Sequence..."); + String lines = File.ReadAllText(path); + //var sequence = JsonConvert.DeserializeObject(lines); + List sequence = System.Text.Json.JsonSerializer.Deserialize>(lines); + + return sequence; + } + + /// + /// Creates list of Sequence as per configuration + /// + /// Object of list of Sequence + public static List CreateDataset() + { + int numberOfSequence = 30; + int size = 12; + int startVal = 0; + int endVal = 15; + Console.WriteLine("Creating Sequence..."); + List sequence = HelperMethods.CreateSequences(numberOfSequence, size, startVal, endVal); + + return sequence; + } + + /// + /// Saves the dataset in 'dataset' folder in BasePath of application + /// + /// Object of list of Sequence + /// Full path of the dataset + public static string SaveDataset(List sequences) + { + string BasePath = AppDomain.CurrentDomain.BaseDirectory; + string reportFolder = Path.Combine(BasePath, "dataset"); + if (!Directory.Exists(reportFolder)) + Directory.CreateDirectory(reportFolder); + string reportPath = Path.Combine(reportFolder, $"dataset_{DateTime.Now.Ticks}.json"); + + Console.WriteLine("Saving dataset..."); + + if (!File.Exists(reportPath)) + { + using (StreamWriter sw = File.CreateText(reportPath)) + { + /*sw.WriteLine("name, data"); + foreach (Sequence sequence in sequences) + { + sw.WriteLine($"{sequence.name}, {string.Join(",", sequence.data)}"); + }*/ + //sw.WriteLine(System.Text.Json.JsonSerializer.Serialize>(sequences)); + sw.WriteLine(JsonConvert.SerializeObject(sequences)); + } + } + + return reportPath; + } + + /// + /// Creats multiple sequences as per parameters + /// + /// Number of sequences to be created + /// Size of each sequence + /// Minimum value of item in a sequence + /// Maximum value of item in a sequence + /// Object of list of Sequence + public static List CreateSequences(int count, int size, int startVal, int stopVal) + { + List dataset = new List(); + + for (int i = 0; i < count; i++) + { + Sequence sequence = new Sequence(); + sequence.name = $"S{i+1}"; + sequence.data = getSyntheticData(size, startVal, stopVal); + dataset.Add(sequence); + } + + return dataset; + } + + /// + /// Creates a sequence of given size-3 and range + /// + /// Size of list + /// Min range of the list + /// Max range of the list + /// + private static int[] getSyntheticData(int size, int startVal, int stopVal) + { + int[] data = new int[size]; + + data = randomRemoveDouble(randomDouble(size, startVal, stopVal), 3); + + return data; + } + + /// + /// Creates a sorted list of array with given paramerters + /// + /// Size of array + /// Min range of the list + /// Max range of the list + /// + private static int[] randomDouble(int size, int startVal, int stopVal) + { + int[] array = new int[size]; + List list = new List(); + int number = 0; + Random r = new Random(Guid.NewGuid().GetHashCode()); + while(list.Count < size) + { + number = r.Next(startVal,stopVal); + if (!list.Contains(number)) + { + if(number >= startVal && number <= stopVal) + list.Add(number); + } + } + + array = list.ToArray(); + Array.Sort(array); + + return array; + } + + /// + /// Randomly remove less number of items from array + /// + /// array to processed + /// number of removals to be done + /// array with less numbers + private static int[] randomRemoveDouble(int[] array, int less) + { + int[] temp = new int[array.Length - less]; + Random random = new Random(Guid.NewGuid().GetHashCode()); + int number = 0; + List list = new List(); + + while (list.Count < (array.Length - less)) + { + number = array[random.Next(0, (array.Length))]; + if (!list.Contains(number)) + list.Add(number); + } + + temp = list.ToArray(); + Array.Sort(temp); + + return temp; + } + + private static int getDigits(int n) + { + if (n >= 0) + { + if (n < 100) return 2; + if (n < 1000) return 3; + if (n < 10000) return 4; + if (n < 100000) return 5; + if (n < 1000000) return 6; + if (n < 10000000) return 7; + if (n < 100000000) return 8; + if (n < 1000000000) return 9; + return 10; + } + else + { + return 2; + } + } + } +} \ No newline at end of file diff --git a/source/Samples/ApproveMultiSequenceLearning/MultiSequenceLearning.csproj b/source/Samples/ApproveMultiSequenceLearning/MultiSequenceLearning.csproj new file mode 100644 index 000000000..f1e8cb154 --- /dev/null +++ b/source/Samples/ApproveMultiSequenceLearning/MultiSequenceLearning.csproj @@ -0,0 +1,20 @@ + + + + Exe + net6.0 + enable + enable + + + + + + + + + + + + + diff --git a/source/Samples/ApproveMultiSequenceLearning/MultisequenceLearning.cs b/source/Samples/ApproveMultiSequenceLearning/MultisequenceLearning.cs new file mode 100644 index 000000000..6759ae060 --- /dev/null +++ b/source/Samples/ApproveMultiSequenceLearning/MultisequenceLearning.cs @@ -0,0 +1,321 @@ +using System; +using System.Globalization; +using System.IO; +using System.Collections.Generic; +using System.Linq; +using System.Text; +using System.Threading.Tasks; +using NeoCortexApi; +using NeoCortexApi.Classifiers; +using NeoCortexApi.Encoders; +using NeoCortexApi.Entities; +using NeoCortexApi.Classifiers; +using NeoCortexApi.Network; +using System; +using System.Collections.Generic; +using System.Diagnostics; +using System.Linq; + + +namespace MultiSequenceLearning +{ + /// + /// Implements an experiment that demonstrates how to learn sequences. + /// + public class MultiSequenceLearning + { + /// + /// Runs the learning of sequences. + /// + /// Dictionary of sequences. KEY is the sewuence name, the VALUE is th elist of element of the sequence. + public Predictor Run(List sequences) + { + Console.WriteLine($"Hello NeocortexApi! Experiment {nameof(MultiSequenceLearning)}"); + + int inputBits = 100; + int numColumns = 1024; + + HtmConfig cfg = HelperMethods.FetchHTMConfig(inputBits, numColumns); + + EncoderBase encoder = HelperMethods.GetEncoder(inputBits); + + return RunExperiment(inputBits, cfg, encoder, sequences); + } + + /// + /// + /// + private Predictor RunExperiment(int inputBits, HtmConfig cfg, EncoderBase encoder, List sequences) + { + Stopwatch sw = new Stopwatch(); + sw.Start(); + + int maxMatchCnt = 0; + + var mem = new Connections(cfg); + + bool isInStableState = false; + + HtmClassifier cls = new HtmClassifier(); + + var numUniqueInputs = GetNumberOfInputs(sequences); + + CortexLayer layer1 = new CortexLayer("L1"); + + TemporalMemory tm = new TemporalMemory(); + + Console.WriteLine("------------ START ------------"); + + // For more information see following paper: https://www.scitepress.org/Papers/2021/103142/103142.pdf + HomeostaticPlasticityController hpc = new HomeostaticPlasticityController(mem, numUniqueInputs * 150, (isStable, numPatterns, actColAvg, seenInputs) => + { + if (isStable) + // Event should be fired when entering the stable state. + Debug.WriteLine($"STABLE: Patterns: {numPatterns}, Inputs: {seenInputs}, iteration: {seenInputs / numPatterns}"); + else + // Ideal SP should never enter unstable state after stable state. + Debug.WriteLine($"INSTABLE: Patterns: {numPatterns}, Inputs: {seenInputs}, iteration: {seenInputs / numPatterns}"); + + // We are not learning in instable state. + isInStableState = isStable; + + // Clear active and predictive cells. + //tm.Reset(mem); + }, numOfCyclesToWaitOnChange: 50); + + + SpatialPoolerMT sp = new SpatialPoolerMT(hpc); + sp.Init(mem); + tm.Init(mem); + + // Please note that we do not add here TM in the layer. + // This is omitted for practical reasons, because we first eneter the newborn-stage of the algorithm + // In this stage we want that SP get boosted and see all elements before we start learning with TM. + // All would also work fine with TM in layer, but it would work much slower. + // So, to improve the speed of experiment, we first ommit the TM and then after the newborn-stage we add it to the layer. + layer1.HtmModules.Add("encoder", encoder); + layer1.HtmModules.Add("sp", sp); + + //double[] inputs = inputValues.ToArray(); + int[] prevActiveCols = new int[0]; + + int cycle = 0; + int matches = 0; + + var lastPredictedValues = new List(new string[] { "0" }); + + int maxCycles = 3500; + + // + // Training SP to get stable. New-born stage. + // + + for (int i = 0; i < maxCycles && isInStableState == false; i++) + { + matches = 0; + + cycle++; + + Debug.WriteLine($"-------------- Newborn SP Cycle {cycle} ---------------"); + Console.WriteLine($"-------------- Newborn SP Cycle {cycle} ---------------"); + + foreach (var inputs in sequences) + { + foreach (var input in inputs.data) + { + Debug.WriteLine($" -- {inputs.name} - {input} --"); + + var lyrOut = layer1.Compute(input, true); + + if (isInStableState) + break; + } + + if (isInStableState) + break; + } + } + + // Clear all learned patterns in the classifier. + cls.ClearState(); + + // We activate here the Temporal Memory algorithm. + layer1.HtmModules.Add("tm", tm); + + // + // Loop over all sequences. + foreach (var sequenceKeyPair in sequences) + { + Debug.WriteLine($"-------------- Sequences {sequenceKeyPair.name} ---------------"); + Console.WriteLine($"-------------- Sequences {sequenceKeyPair.name} ---------------"); + + int maxPrevInputs = sequenceKeyPair.data.Length - 1; + + List previousInputs = new List(); + + previousInputs.Add("-1"); + + // + // Now training with SP+TM. SP is pretrained on the given input pattern set. + for (int i = 0; i < maxCycles; i++) + { + matches = 0; + + cycle++; + + Debug.WriteLine(""); + + Debug.WriteLine($"-------------- Cycle SP+TM{cycle} ---------------"); + Console.WriteLine($"-------------- Cycle SP+TM {cycle} ---------------"); + + foreach (var input in sequenceKeyPair.data) + { + Debug.WriteLine($"-------------- {input} ---------------"); + + var lyrOut = layer1.Compute(input, true) as ComputeCycle; + + var activeColumns = layer1.GetResult("sp") as int[]; + + previousInputs.Add(input.ToString()); + if (previousInputs.Count > (maxPrevInputs + 1)) + previousInputs.RemoveAt(0); + + // In the pretrained SP with HPC, the TM will quickly learn cells for patterns + // In that case the starting sequence 4-5-6 might have the sam SDR as 1-2-3-4-5-6, + // Which will result in returning of 4-5-6 instead of 1-2-3-4-5-6. + // HtmClassifier allways return the first matching sequence. Because 4-5-6 will be as first + // memorized, it will match as the first one. + if (previousInputs.Count < maxPrevInputs) + continue; + + string key = GetKey(previousInputs, input, sequenceKeyPair.name); + + List actCells; + + if (lyrOut.ActiveCells.Count == lyrOut.WinnerCells.Count) + { + actCells = lyrOut.ActiveCells; + } + else + { + actCells = lyrOut.WinnerCells; + } + + cls.Learn(key, actCells.ToArray()); + + Debug.WriteLine($"Col SDR: {Helpers.StringifyVector(lyrOut.ActivColumnIndicies)}"); + Debug.WriteLine($"Cell SDR: {Helpers.StringifyVector(actCells.Select(c => c.Index).ToArray())}"); + + // + // If the list of predicted values from the previous step contains the currently presenting value, + // we have a match. + if (lastPredictedValues.Contains(key)) + { + matches++; + Debug.WriteLine($"Match. Actual value: {key} - Predicted value: {lastPredictedValues.FirstOrDefault(key)}."); + } + else + Debug.WriteLine($"Missmatch! Actual value: {key} - Predicted values: {String.Join(',', lastPredictedValues)}"); + + if (lyrOut.PredictiveCells.Count > 0) + { + //var predictedInputValue = cls.GetPredictedInputValue(lyrOut.PredictiveCells.ToArray()); + var predictedInputValues = cls.GetPredictedInputValues(lyrOut.PredictiveCells.ToArray(), 3); + + foreach (var item in predictedInputValues) + { + Debug.WriteLine($"Current Input: {input} \t| Predicted Input: {item.PredictedInput} - {item.Similarity}"); + } + + lastPredictedValues = predictedInputValues.Select(v => v.PredictedInput).ToList(); + } + else + { + Debug.WriteLine($"NO CELLS PREDICTED for next cycle."); + lastPredictedValues = new List(); + } + } + + // The first element (a single element) in the sequence cannot be predicted + double maxPossibleAccuraccy = (double)((double)sequenceKeyPair.data.Length - 1) / (double)sequenceKeyPair.data.Length * 100.0; + + double accuracy = (double)matches / (double)sequenceKeyPair.data.Length * 100.0; + + Debug.WriteLine($"Cycle: {cycle}\tMatches={matches} of {sequenceKeyPair.data.Length}\t {accuracy}%"); + Console.WriteLine($"Cycle: {cycle}\tMatches={matches} of {sequenceKeyPair.data.Length}\t {accuracy}%"); + + if (accuracy >= maxPossibleAccuraccy) + { + maxMatchCnt++; + Debug.WriteLine($"100% accuracy reched {maxMatchCnt} times."); + + // + // Experiment is completed if we are 30 cycles long at the 100% accuracy. + if (maxMatchCnt >= 30) + { + sw.Stop(); + Debug.WriteLine($"Sequence learned. The algorithm is in the stable state after 30 repeats with with accuracy {accuracy} of maximum possible {maxMatchCnt}. Elapsed sequence {sequenceKeyPair.name} learning time: {sw.Elapsed}."); + break; + } + } + else if (maxMatchCnt > 0) + { + Debug.WriteLine($"At 100% accuracy after {maxMatchCnt} repeats we get a drop of accuracy with accuracy {accuracy}. This indicates instable state. Learning will be continued."); + maxMatchCnt = 0; + } + + // This resets the learned state, so the first element starts allways from the beginning. + tm.Reset(mem); + } + } + + Debug.WriteLine("------------ END ------------"); + + return new Predictor(layer1, mem, cls); + } + + + /// + /// Gets the number of all unique inputs. + /// + /// Alle sequences. + /// + private int GetNumberOfInputs(List sequences) + { + int num = 0; + + foreach (var inputs in sequences) + { + //num += inputs.Value.Distinct().Count(); + num += inputs.data.Length; + } + + return num; + } + + + /// + /// Constracts the unique key of the element of an sequece. This key is used as input for HtmClassifier. + /// It makes sure that alle elements that belong to the same sequence are prefixed with the sequence. + /// The prediction code can then extract the sequence prefix to the predicted element. + /// + /// + /// + /// + /// + private static string GetKey(List prevInputs, double input, string sequence) + { + string key = String.Empty; + + for (int i = 0; i < prevInputs.Count; i++) + { + if (i > 0) + key += "-"; + + key += (prevInputs[i]); + } + //Console.WriteLine($"GetKey={sequence}_{key}"); + return $"{sequence}_{key}"; + } + } +} diff --git a/source/Samples/ApproveMultiSequenceLearning/Program.cs b/source/Samples/ApproveMultiSequenceLearning/Program.cs new file mode 100644 index 000000000..1f1d3bd67 --- /dev/null +++ b/source/Samples/ApproveMultiSequenceLearning/Program.cs @@ -0,0 +1,228 @@ +using NeoCortexApi; +using NeoCortexApi.Encoders; +using Newtonsoft.Json; +using System; +using System.Collections.Generic; +using System.Diagnostics; +using System.Linq; +using static MultiSequenceLearning.MultiSequenceLearning; + +namespace MultiSequenceLearning +{ + class Program + { + /// + /// This sample shows a typical experiment code for SP and TM. + /// You must start this code in debugger to follow the trace. + /// and TM. + /// + /// + static void Main(string[] args) + { + + //to create synthetic dataset + /*string path = HelperMethods.SaveDataset(HelperMethods.CreateDataset()); + Console.WriteLine($"Dataset saved: {path}");*/ + + //to read dataset + string BasePath = AppDomain.CurrentDomain.BaseDirectory; + string datasetPath = Path.Combine(BasePath, "dataset", "dataset_03.json"); + Console.WriteLine($"Reading Dataset: {datasetPath}"); + List sequences = HelperMethods.ReadDataset(datasetPath); + + //to read test dataset + string testsetPath = Path.Combine(BasePath, "dataset", "test_01.json"); + Console.WriteLine($"Reading Testset: {testsetPath}"); + List sequencesTest = HelperMethods.ReadDataset(testsetPath); + + //run learing only + //RunSimpleMultiSequenceLearningExperiment(sequences); + + //run learning + prediction and generates report for results + List reports = RunMultiSequenceLearningExperiment(sequences, sequencesTest); + + WriteReport(sequences, reports); + + Console.WriteLine("Done..."); + + } + + /// + /// write and formats data in report object to a file + /// + /// input sequence + /// object of report + private static void WriteReport(List sequences, List reports) + { + string BasePath = AppDomain.CurrentDomain.BaseDirectory; + string reportFolder = Path.Combine(BasePath, "report"); + if (!Directory.Exists(reportFolder)) + Directory.CreateDirectory(reportFolder); + string reportPath = Path.Combine(reportFolder, $"report_{DateTime.Now.Ticks}.txt"); + + if (!File.Exists(reportPath)) + { + using (StreamWriter sw = File.CreateText(reportPath)) + { + sw.WriteLine("------------------------------"); + foreach (Sequence sequence in sequences) + { + sw.WriteLine($"Sequence: {sequence.name} -> {string.Join("-",sequence.data)}"); + } + sw.WriteLine("------------------------------"); + foreach (Report report in reports) + { + sw.WriteLine($"Using test sequence: {report.SequenceName} -> {string.Join("-",report.SequenceData)}"); + foreach (string log in report.PredictionLog) + { + sw.WriteLine($"\t{log}"); + } + sw.WriteLine($"\tAccuracy: {report.Accuracy}%"); + sw.WriteLine("------------------------------"); + } + } + } + + } + + /// + /// takes input data set and runs the alogrithm + /// + /// input test dataset + private static void RunSimpleMultiSequenceLearningExperiment(List sequences) + { + // + // Prototype for building the prediction engine. + MultiSequenceLearning experiment = new MultiSequenceLearning(); + var predictor = experiment.Run(sequences); + } + + + /// + /// This example demonstrates how to learn two sequences and how to use the prediction mechanism. + /// First, two sequences are learned. + /// Second, three short sequences with three elements each are created und used for prediction. The predictor used by experiment privides to the HTM every element of every predicting sequence. + /// The predictor tries to predict the next element. + /// + /// input dataset + /// input test dataset + /// list of Report per sequence + private static List RunMultiSequenceLearningExperiment(List sequences, List sequencesTest) + { + List reports = new List(); + Report report = new Report(); + + // Prototype for building the prediction engine. + MultiSequenceLearning experiment = new MultiSequenceLearning(); + var predictor = experiment.Run(sequences); + + // These list are used to see how the prediction works. + // Predictor is traversing the list element by element. + // By providing more elements to the prediction, the predictor delivers more precise result. + + foreach (Sequence item in sequencesTest) + { + report.SequenceName = item.name; + Debug.WriteLine($"Using test sequence: {item.name}"); + Console.WriteLine("------------------------------"); + Console.WriteLine($"Using test sequence: {item.name}"); + predictor.Reset(); + report.SequenceData = item.data; + var accuracy = PredictNextElement(predictor, item.data, report); + reports.Add(report); + Console.WriteLine($"Accuracy for {item.name} sequence: {accuracy}%"); + } + + return reports; + + } + + /// + /// Takes predicted model, subsequence and generates report stating accuracy + /// + /// Object of Predictor + /// sub-sequence to be tested + /// accuracy of predicting elements in % + private static double PredictNextElement(Predictor predictor, int[] list, Report report) + { + int matchCount = 0; + int predictions = 0; + double accuracy = 0.0; + List logs = new List(); + Console.WriteLine("------------------------------"); + + int prev = -1; + bool first = true; + + /* + * Pseudo code for calculating accuracy: + * + * 1. loop for each element in the sub-sequence + * 1.1 if the element is first element do nothing and save the element as 'previous' for further comparision + * 1.2 take previous element and predict the next element + * 1.2.1 get the predicted element with highest similarity and compare with 'next' element + * 1.2.1.1 if predicted element matches the next element increment the counter of matched elements + * 1.2.2 increment the count for number of calls made to predict an element + * 1.2 update the 'previous' element with 'next' element + * 2. calculate the accuracy as (number of matched elements)/(total number of calls for prediction) * 100 + * 3. end the loop + */ + + foreach (var next in list) + { + if(first) + { + first = false; + } + else + { + Console.WriteLine($"Input: {prev}"); + var res = predictor.Predict(prev); + string log = ""; + if (res.Count > 0) + { + foreach (var pred in res) + { + Debug.WriteLine($"Predicted Input: {pred.PredictedInput} - Similarity: {pred.Similarity}%"); + } + + var sequence = res.First().PredictedInput.Split('_'); + var prediction = res.First().PredictedInput.Split('-'); + Console.WriteLine($"Predicted Sequence: {sequence.First()} - Predicted next element: {prediction.Last()}"); + log = $"Input: {prev}, Predicted Sequence: {sequence.First()}, Predicted next element: {prediction.Last()}"; + //compare current element with prediction of previous element + if(next == Int32.Parse(prediction.Last())) + { + matchCount++; + } + } + else + { + Console.WriteLine("Nothing predicted :("); + log = $"Input: {prev}, Nothing predicted"; + } + + logs.Add(log); + predictions++; + } + + //save previous element to compare with upcoming element + prev = next; + } + + report.PredictionLog = logs; + + /* + * Accuracy is calculated as number of matching predictions made + * divided by total number of prediction made for an element in subsequence + * + * accuracy = number of matching predictions/total number of prediction * 100 + */ + accuracy = (double)matchCount / predictions * 100; + report.Accuracy = accuracy; + Console.WriteLine("------------------------------"); + + return accuracy; + } + } +} diff --git a/source/Samples/ApproveMultiSequenceLearning/Report.cs b/source/Samples/ApproveMultiSequenceLearning/Report.cs new file mode 100644 index 000000000..0bc901651 --- /dev/null +++ b/source/Samples/ApproveMultiSequenceLearning/Report.cs @@ -0,0 +1,20 @@ +using Org.BouncyCastle.Asn1.Mozilla; +using System; +using System.Collections.Generic; +using System.Linq; +using System.Text; +using System.Threading.Tasks; + +namespace MultiSequenceLearning +{ + public class Report + { + public Report() { } + + public string SequenceName { get; set; } + public int[] SequenceData { get; set; } + public List PredictionLog { get; set; } + public double Accuracy { get; set; } + + } +} diff --git a/source/Samples/ApproveMultiSequenceLearning/Sequence.cs b/source/Samples/ApproveMultiSequenceLearning/Sequence.cs new file mode 100644 index 000000000..d8fcb7732 --- /dev/null +++ b/source/Samples/ApproveMultiSequenceLearning/Sequence.cs @@ -0,0 +1,14 @@ +using System; +using System.Collections.Generic; +using System.Linq; +using System.Text; +using System.Threading.Tasks; + +namespace MultiSequenceLearning +{ + public class Sequence + { + public String name { get; set; } + public int[] data { get; set; } + } +} diff --git a/source/Samples/ApproveMultiSequenceLearning/report/report_638172569568213902.txt b/source/Samples/ApproveMultiSequenceLearning/report/report_638172569568213902.txt new file mode 100644 index 000000000..ee1234df5 --- /dev/null +++ b/source/Samples/ApproveMultiSequenceLearning/report/report_638172569568213902.txt @@ -0,0 +1,33 @@ +------------------------------ +Sequence: S1 -> 0-2-5-6-7-8-10-11-13 +Sequence: S2 -> 1-2-3-4-6-11-12-13-14 +Sequence: S3 -> 1-2-3-4-7-8-10-12-14 +------------------------------ +Using test sequence: T4 -> 3-4-7-8-10 + Input: 3, Predicted Sequence: S2, Predicted next element: 4 + Input: 4, Predicted Sequence: S3, Predicted next element: 7 + Input: 7, Predicted Sequence: S3, Predicted next element: 8 + Input: 8, Predicted Sequence: S3, Predicted next element: 10 + Accuracy: 100% +------------------------------ +Using test sequence: T4 -> 3-4-7-8-10 + Input: 3, Predicted Sequence: S2, Predicted next element: 4 + Input: 4, Predicted Sequence: S3, Predicted next element: 7 + Input: 7, Predicted Sequence: S3, Predicted next element: 8 + Input: 8, Predicted Sequence: S3, Predicted next element: 10 + Accuracy: 100% +------------------------------ +Using test sequence: T4 -> 3-4-7-8-10 + Input: 3, Predicted Sequence: S2, Predicted next element: 4 + Input: 4, Predicted Sequence: S3, Predicted next element: 7 + Input: 7, Predicted Sequence: S3, Predicted next element: 8 + Input: 8, Predicted Sequence: S3, Predicted next element: 10 + Accuracy: 100% +------------------------------ +Using test sequence: T4 -> 3-4-7-8-10 + Input: 3, Predicted Sequence: S2, Predicted next element: 4 + Input: 4, Predicted Sequence: S3, Predicted next element: 7 + Input: 7, Predicted Sequence: S3, Predicted next element: 8 + Input: 8, Predicted Sequence: S3, Predicted next element: 10 + Accuracy: 100% +------------------------------ diff --git a/source/Samples/ApproveMultiSequenceLearning/report/report_638172649322130486.txt b/source/Samples/ApproveMultiSequenceLearning/report/report_638172649322130486.txt new file mode 100644 index 000000000..89d715b70 --- /dev/null +++ b/source/Samples/ApproveMultiSequenceLearning/report/report_638172649322130486.txt @@ -0,0 +1,60 @@ +------------------------------ +Sequence: S1 -> 0-1-4-5-6-7-8-13-14 +Sequence: S2 -> 0-1-2-3-4-5-7-8-11 +Sequence: S3 -> 0-1-2-3-4-5-6-9-13 +Sequence: S4 -> 1-3-5-8-9-10-11-12-14 +Sequence: S5 -> 0-3-4-6-7-10-12-13-14 +Sequence: S6 -> 2-3-4-5-7-8-9-10-14 +Sequence: S7 -> 0-2-3-4-5-6-8-10-13 +Sequence: S8 -> 1-2-4-5-6-10-11-12-13 +Sequence: S9 -> 0-1-2-3-4-8-10-12-13 +Sequence: S10 -> 0-1-2-4-5-6-7-9-11 +Sequence: S11 -> 2-3-4-9-10-11-12-13-14 +Sequence: S12 -> 0-2-3-4-5-6-7-12-13 +Sequence: S13 -> 1-2-4-6-8-9-11-12-14 +Sequence: S14 -> 2-4-6-8-9-10-11-12-13 +Sequence: S15 -> 0-1-3-6-8-9-10-13-14 +Sequence: S16 -> 1-2-3-8-9-10-11-12-14 +Sequence: S17 -> 0-1-3-4-6-7-9-10-13 +Sequence: S18 -> 1-3-5-6-8-9-10-12-13 +Sequence: S19 -> 1-2-4-5-6-10-11-12-14 +Sequence: S20 -> 0-2-4-5-6-9-10-13-14 +Sequence: S21 -> 0-4-5-6-7-8-11-13-14 +Sequence: S22 -> 0-3-5-6-7-8-9-10-13 +Sequence: S23 -> 0-1-2-3-5-7-9-10-13 +Sequence: S24 -> 0-3-4-6-8-9-10-13-14 +Sequence: S25 -> 0-1-2-3-5-8-10-12-13 +Sequence: S26 -> 0-3-5-6-8-9-10-12-13 +Sequence: S27 -> 1-3-4-5-6-8-11-12-14 +Sequence: S28 -> 0-3-5-6-7-9-11-12-13 +Sequence: S29 -> 0-2-3-4-5-6-11-12-14 +Sequence: S30 -> 0-1-2-3-4-6-8-9-12 +------------------------------ +Using test sequence: T4 -> 3-4-7-8-10 + Input: 3, Predicted Sequence: S24, Predicted next element: 4 + Input: 4, Predicted Sequence: S30, Predicted next element: 6 + Input: 7, Predicted Sequence: S17, Predicted next element: 9 + Input: 8, Predicted Sequence: S30, Predicted next element: 9 + Accuracy: 25% +------------------------------ +Using test sequence: T4 -> 3-4-7-8-10 + Input: 3, Predicted Sequence: S24, Predicted next element: 4 + Input: 4, Predicted Sequence: S30, Predicted next element: 6 + Input: 7, Predicted Sequence: S17, Predicted next element: 9 + Input: 8, Predicted Sequence: S30, Predicted next element: 9 + Accuracy: 25% +------------------------------ +Using test sequence: T4 -> 3-4-7-8-10 + Input: 3, Predicted Sequence: S24, Predicted next element: 4 + Input: 4, Predicted Sequence: S30, Predicted next element: 6 + Input: 7, Predicted Sequence: S17, Predicted next element: 9 + Input: 8, Predicted Sequence: S30, Predicted next element: 9 + Accuracy: 25% +------------------------------ +Using test sequence: T4 -> 3-4-7-8-10 + Input: 3, Predicted Sequence: S24, Predicted next element: 4 + Input: 4, Predicted Sequence: S30, Predicted next element: 6 + Input: 7, Predicted Sequence: S17, Predicted next element: 9 + Input: 8, Predicted Sequence: S30, Predicted next element: 9 + Accuracy: 25% +------------------------------ From f6371b9a4ad1a86d73575ea8d27b2c8a241eb523 Mon Sep 17 00:00:00 2001 From: Mahdieh Pirmoradian Date: Sun, 21 Jan 2024 21:10:32 +0100 Subject: [PATCH 12/16] Modifying the code --- .../ApproveMultiSequenceLearning/Program.cs | 264 +++++++----------- 1 file changed, 94 insertions(+), 170 deletions(-) diff --git a/source/Samples/ApproveMultiSequenceLearning/Program.cs b/source/Samples/ApproveMultiSequenceLearning/Program.cs index 1f1d3bd67..6c6b427df 100644 --- a/source/Samples/ApproveMultiSequenceLearning/Program.cs +++ b/source/Samples/ApproveMultiSequenceLearning/Program.cs @@ -11,218 +11,142 @@ namespace MultiSequenceLearning { class Program { - /// - /// This sample shows a typical experiment code for SP and TM. - /// You must start this code in debugger to follow the trace. - /// and TM. - /// - /// + private const string DatasetFolder = "dataset"; + private const string ReportFolder = "report"; + private const string DatasetFileName = "dataset_03.json"; + private const string TestsetFileName = "test_01.json"; + static void Main(string[] args) { - - //to create synthetic dataset - /*string path = HelperMethods.SaveDataset(HelperMethods.CreateDataset()); - Console.WriteLine($"Dataset saved: {path}");*/ - - //to read dataset - string BasePath = AppDomain.CurrentDomain.BaseDirectory; - string datasetPath = Path.Combine(BasePath, "dataset", "dataset_03.json"); - Console.WriteLine($"Reading Dataset: {datasetPath}"); - List sequences = HelperMethods.ReadDataset(datasetPath); - + //to read Input Dataset + string basePath = AppDomain.CurrentDomain.BaseDirectory; + List sequences = ReadDataset(Path.Combine(basePath, DatasetFolder, DatasetFileName)); //to read test dataset - string testsetPath = Path.Combine(BasePath, "dataset", "test_01.json"); - Console.WriteLine($"Reading Testset: {testsetPath}"); - List sequencesTest = HelperMethods.ReadDataset(testsetPath); - - //run learing only - //RunSimpleMultiSequenceLearningExperiment(sequences); + List sequencesTest = ReadDataset(Path.Combine(basePath, DatasetFolder, TestsetFileName)); - //run learning + prediction and generates report for results List reports = RunMultiSequenceLearningExperiment(sequences, sequencesTest); + WriteReport(reports, basePath); + } - WriteReport(sequences, reports); - - Console.WriteLine("Done..."); - + private static List ReadDataset(string datasetPath) + { + try + { + Console.WriteLine($"Reading Dataset: {datasetPath}"); + return JsonConvert.DeserializeObject>(File.ReadAllText(datasetPath)); + } + catch (Exception ex) + { + Console.WriteLine($"Error reading dataset: {ex.Message}"); + return new List(); + } } - /// - /// write and formats data in report object to a file - /// - /// input sequence - /// object of report - private static void WriteReport(List sequences, List reports) + private static void WriteReport(List reports, string basePath) { - string BasePath = AppDomain.CurrentDomain.BaseDirectory; - string reportFolder = Path.Combine(BasePath, "report"); - if (!Directory.Exists(reportFolder)) - Directory.CreateDirectory(reportFolder); + string reportFolder = EnsureDirectory(Path.Combine(basePath, ReportFolder)); string reportPath = Path.Combine(reportFolder, $"report_{DateTime.Now.Ticks}.txt"); - if (!File.Exists(reportPath)) + using (StreamWriter sw = File.CreateText(reportPath)) { - using (StreamWriter sw = File.CreateText(reportPath)) + foreach (Report report in reports) { - sw.WriteLine("------------------------------"); - foreach (Sequence sequence in sequences) - { - sw.WriteLine($"Sequence: {sequence.name} -> {string.Join("-",sequence.data)}"); - } - sw.WriteLine("------------------------------"); - foreach (Report report in reports) - { - sw.WriteLine($"Using test sequence: {report.SequenceName} -> {string.Join("-",report.SequenceData)}"); - foreach (string log in report.PredictionLog) - { - sw.WriteLine($"\t{log}"); - } - sw.WriteLine($"\tAccuracy: {report.Accuracy}%"); - sw.WriteLine("------------------------------"); - } + WriteReportContent(sw, report); } } - } - /// - /// takes input data set and runs the alogrithm - /// - /// input test dataset - private static void RunSimpleMultiSequenceLearningExperiment(List sequences) + private static string EnsureDirectory(string path) { - // - // Prototype for building the prediction engine. - MultiSequenceLearning experiment = new MultiSequenceLearning(); - var predictor = experiment.Run(sequences); + if (!Directory.Exists(path)) + Directory.CreateDirectory(path); + return path; } + private static void WriteReportContent(StreamWriter sw, Report report) + { + sw.WriteLine("------------------------------"); + sw.WriteLine($"Using test sequence: {report.SequenceName} -> {string.Join("-", report.SequenceData)}"); + foreach (string log in report.PredictionLog) + { + sw.WriteLine($"\t{log}"); + } + sw.WriteLine($"\tAccuracy: {report.Accuracy}%"); + sw.WriteLine("------------------------------"); + } - /// - /// This example demonstrates how to learn two sequences and how to use the prediction mechanism. - /// First, two sequences are learned. - /// Second, three short sequences with three elements each are created und used for prediction. The predictor used by experiment privides to the HTM every element of every predicting sequence. - /// The predictor tries to predict the next element. - /// - /// input dataset - /// input test dataset - /// list of Report per sequence private static List RunMultiSequenceLearningExperiment(List sequences, List sequencesTest) { - List reports = new List(); - Report report = new Report(); - - // Prototype for building the prediction engine. - MultiSequenceLearning experiment = new MultiSequenceLearning(); + var reports = new List(); + var experiment = new MultiSequenceLearning(); var predictor = experiment.Run(sequences); - // These list are used to see how the prediction works. - // Predictor is traversing the list element by element. - // By providing more elements to the prediction, the predictor delivers more precise result. - foreach (Sequence item in sequencesTest) { - report.SequenceName = item.name; - Debug.WriteLine($"Using test sequence: {item.name}"); - Console.WriteLine("------------------------------"); - Console.WriteLine($"Using test sequence: {item.name}"); - predictor.Reset(); - report.SequenceData = item.data; - var accuracy = PredictNextElement(predictor, item.data, report); + var report = new Report + { + SequenceName = item.name, + SequenceData = item.data + }; + + double accuracy = PredictNextElement(predictor, item.data, report); + report.Accuracy = accuracy; reports.Add(report); + Console.WriteLine($"Accuracy for {item.name} sequence: {accuracy}%"); } return reports; - } - /// - /// Takes predicted model, subsequence and generates report stating accuracy - /// - /// Object of Predictor - /// sub-sequence to be tested - /// accuracy of predicting elements in % private static double PredictNextElement(Predictor predictor, int[] list, Report report) { - int matchCount = 0; - int predictions = 0; - double accuracy = 0.0; + int matchCount = 0, predictions = 0; List logs = new List(); - Console.WriteLine("------------------------------"); - - int prev = -1; - bool first = true; - - /* - * Pseudo code for calculating accuracy: - * - * 1. loop for each element in the sub-sequence - * 1.1 if the element is first element do nothing and save the element as 'previous' for further comparision - * 1.2 take previous element and predict the next element - * 1.2.1 get the predicted element with highest similarity and compare with 'next' element - * 1.2.1.1 if predicted element matches the next element increment the counter of matched elements - * 1.2.2 increment the count for number of calls made to predict an element - * 1.2 update the 'previous' element with 'next' element - * 2. calculate the accuracy as (number of matched elements)/(total number of calls for prediction) * 100 - * 3. end the loop - */ - - foreach (var next in list) + + predictor.Reset(); + + for (int i = 0; i < list.Length - 1; i++) { - if(first) - { - first = false; - } - else - { - Console.WriteLine($"Input: {prev}"); - var res = predictor.Predict(prev); - string log = ""; - if (res.Count > 0) - { - foreach (var pred in res) - { - Debug.WriteLine($"Predicted Input: {pred.PredictedInput} - Similarity: {pred.Similarity}%"); - } - - var sequence = res.First().PredictedInput.Split('_'); - var prediction = res.First().PredictedInput.Split('-'); - Console.WriteLine($"Predicted Sequence: {sequence.First()} - Predicted next element: {prediction.Last()}"); - log = $"Input: {prev}, Predicted Sequence: {sequence.First()}, Predicted next element: {prediction.Last()}"; - //compare current element with prediction of previous element - if(next == Int32.Parse(prediction.Last())) - { - matchCount++; - } - } - else - { - Console.WriteLine("Nothing predicted :("); - log = $"Input: {prev}, Nothing predicted"; - } - - logs.Add(log); - predictions++; - } + int current = list[i]; + int next = list[i + 1]; - //save previous element to compare with upcoming element - prev = next; + logs.Add(PredictElement(predictor, current, next, ref matchCount)); + predictions++; } report.PredictionLog = logs; + return CalculateAccuracy(matchCount, predictions); + } - /* - * Accuracy is calculated as number of matching predictions made - * divided by total number of prediction made for an element in subsequence - * - * accuracy = number of matching predictions/total number of prediction * 100 - */ - accuracy = (double)matchCount / predictions * 100; - report.Accuracy = accuracy; - Console.WriteLine("------------------------------"); - - return accuracy; + private static string PredictElement(Predictor predictor, int current, int next, ref int matchCount) + { + Console.WriteLine($"Input: {current}"); + var predictions = predictor.Predict(current); + if (predictions.Any()) + { + var highestPrediction = predictions.OrderByDescending(p => p.Similarity).First(); + string predictedSequence = highestPrediction.PredictedInput.Split('-').First(); + int predictedNext = int.Parse(highestPrediction.PredictedInput.Split('-').Last()); + + Console.WriteLine($"Predicted Sequence: {predictedSequence} - Predicted next element: {predictedNext}"); + if (predictedNext == next) + matchCount++; + + return $"Input: {current}, Predicted Sequence: {predictedSequence}, Predicted next element: {predictedNext}"; + } + else + { + Console.WriteLine("Nothing predicted"); + return $"Input: {current}, Nothing predicted"; + } + } + + private static double CalculateAccuracy(int matchCount, int predictions) + { + return (double)matchCount / predictions * 100; } } -} + + +} \ No newline at end of file From 23d3b38d5e9f641d3d6d1c63445412c72569ac0c Mon Sep 17 00:00:00 2001 From: Mahdieh Pirmoradian Date: Sun, 21 Jan 2024 22:11:19 +0100 Subject: [PATCH 13/16] Modifying the code related to "ApproveMultiSequenceLearning" Project --- .../HelperMethods.cs | 280 +++++------------- 1 file changed, 71 insertions(+), 209 deletions(-) diff --git a/source/Samples/ApproveMultiSequenceLearning/HelperMethods.cs b/source/Samples/ApproveMultiSequenceLearning/HelperMethods.cs index 84225d7f8..acaa4da22 100644 --- a/source/Samples/ApproveMultiSequenceLearning/HelperMethods.cs +++ b/source/Samples/ApproveMultiSequenceLearning/HelperMethods.cs @@ -15,266 +15,128 @@ namespace MultiSequenceLearning { public class HelperMethods { - public HelperMethods() - { - //needs no implementation - } + // Constants for default settings + private const int DefaultRandomSeed = 42; + private const double MaxScalarValue = 20.0; + + // Avoid magic numbers in your code + private const int DefaultCellsPerColumn = 25; + private const double DefaultGlobalInhibitionDensity = 0.02; + private const double DefaultPotentialRadiusFactor = 0.15; + private const double DefaultMaxSynapsesPerSegmentFactor = 0.02; + private const double DefaultMaxBoost = 10.0; + private const int DefaultDutyCyclePeriod = 25; + private const double DefaultMinPctOverlapDutyCycles = 0.75; + private const int DefaultActivationThreshold = 15; + private const double DefaultConnectedPermanence = 0.5; + private const double DefaultPermanenceDecrement = 0.25; + private const double DefaultPermanenceIncrement = 0.15; + private const double DefaultPredictedSegmentDecrement = 0.1; /// /// HTM Config for creating Connections /// - /// input bits - /// number of columns - /// Object of HTMConfig public static HtmConfig FetchHTMConfig(int inputBits, int numColumns) { - HtmConfig cfg = new HtmConfig(new int[] { inputBits }, new int[] { numColumns }) + return new HtmConfig(new int[] { inputBits }, new int[] { numColumns }) { - Random = new ThreadSafeRandom(42), - - CellsPerColumn = 25, + Random = new ThreadSafeRandom(DefaultRandomSeed), + CellsPerColumn = DefaultCellsPerColumn, GlobalInhibition = true, LocalAreaDensity = -1, - NumActiveColumnsPerInhArea = 0.02 * numColumns, - PotentialRadius = (int)(0.15 * inputBits), - //InhibitionRadius = 15, - - MaxBoost = 10.0, - DutyCyclePeriod = 25, - MinPctOverlapDutyCycles = 0.75, - MaxSynapsesPerSegment = (int)(0.02 * numColumns), - - ActivationThreshold = 15, - ConnectedPermanence = 0.5, - - // Learning is slower than forgetting in this case. - PermanenceDecrement = 0.25, - PermanenceIncrement = 0.15, - - // Used by punishing of segments. - PredictedSegmentDecrement = 0.1, - - //NumInputs = 88 + NumActiveColumnsPerInhArea = DefaultGlobalInhibitionDensity * numColumns, + PotentialRadius = (int)(DefaultPotentialRadiusFactor * inputBits), + MaxBoost = DefaultMaxBoost, + DutyCyclePeriod = DefaultDutyCyclePeriod, + MinPctOverlapDutyCycles = DefaultMinPctOverlapDutyCycles, + MaxSynapsesPerSegment = (int)(DefaultMaxSynapsesPerSegmentFactor * numColumns), + ActivationThreshold = DefaultActivationThreshold, + ConnectedPermanence = DefaultConnectedPermanence, + PermanenceDecrement = DefaultPermanenceDecrement, + PermanenceIncrement = DefaultPermanenceIncrement, + PredictedSegmentDecrement = DefaultPredictedSegmentDecrement }; - - return cfg; - } - - /// - /// Takes in user input and return encoded SDR for prediction - /// - /// - /// - public static int[] EncodeSingleInput(string userInput) - { - int[] sdr = new int[0]; - - //needs no implementation - - return sdr; } /// /// Get the encoder with settings /// - /// input bits - /// Object of EncoderBase public static EncoderBase GetEncoder(int inputBits) { - double max = 20; - - Dictionary settings = new Dictionary() + var settings = new Dictionary { - { "W", 15}, - { "N", inputBits}, - { "Radius", -1.0}, - { "MinVal", 0.0}, - { "Periodic", false}, - { "Name", "scalar"}, - { "ClipInput", false}, - { "MaxVal", max} + { "W", 15 }, + { "N", inputBits }, + { "Radius", -1.0 }, + { "MinVal", 0.0 }, + { "Periodic", false }, + { "Name", "scalar" }, + { "ClipInput", false }, + { "MaxVal", MaxScalarValue } }; - EncoderBase encoder = new ScalarEncoder(settings); - - return encoder; + return new ScalarEncoder(settings); } /// /// Reads dataset from the file /// - /// full path of the file - /// Object of list of Sequence public static List ReadDataset(string path) { Console.WriteLine("Reading Sequence..."); - String lines = File.ReadAllText(path); - //var sequence = JsonConvert.DeserializeObject(lines); - List sequence = System.Text.Json.JsonSerializer.Deserialize>(lines); - - return sequence; - } - - /// - /// Creates list of Sequence as per configuration - /// - /// Object of list of Sequence - public static List CreateDataset() - { - int numberOfSequence = 30; - int size = 12; - int startVal = 0; - int endVal = 15; - Console.WriteLine("Creating Sequence..."); - List sequence = HelperMethods.CreateSequences(numberOfSequence, size, startVal, endVal); - - return sequence; - } - - /// - /// Saves the dataset in 'dataset' folder in BasePath of application - /// - /// Object of list of Sequence - /// Full path of the dataset - public static string SaveDataset(List sequences) - { - string BasePath = AppDomain.CurrentDomain.BaseDirectory; - string reportFolder = Path.Combine(BasePath, "dataset"); - if (!Directory.Exists(reportFolder)) - Directory.CreateDirectory(reportFolder); - string reportPath = Path.Combine(reportFolder, $"dataset_{DateTime.Now.Ticks}.json"); - - Console.WriteLine("Saving dataset..."); - - if (!File.Exists(reportPath)) + try { - using (StreamWriter sw = File.CreateText(reportPath)) - { - /*sw.WriteLine("name, data"); - foreach (Sequence sequence in sequences) - { - sw.WriteLine($"{sequence.name}, {string.Join(",", sequence.data)}"); - }*/ - //sw.WriteLine(System.Text.Json.JsonSerializer.Serialize>(sequences)); - sw.WriteLine(JsonConvert.SerializeObject(sequences)); - } + string fileContent = File.ReadAllText(path); + return JsonConvert.DeserializeObject>(fileContent); } - - return reportPath; - } - - /// - /// Creats multiple sequences as per parameters - /// - /// Number of sequences to be created - /// Size of each sequence - /// Minimum value of item in a sequence - /// Maximum value of item in a sequence - /// Object of list of Sequence - public static List CreateSequences(int count, int size, int startVal, int stopVal) - { - List dataset = new List(); - - for (int i = 0; i < count; i++) + catch (Exception ex) { - Sequence sequence = new Sequence(); - sequence.name = $"S{i+1}"; - sequence.data = getSyntheticData(size, startVal, stopVal); - dataset.Add(sequence); + Console.WriteLine($"Failed to read the dataset: {ex.Message}"); + return new List(); // Return an empty list in case of failure } - - return dataset; } /// - /// Creates a sequence of given size-3 and range + /// Saves the dataset in 'dataset' folder in BasePath of application /// - /// Size of list - /// Min range of the list - /// Max range of the list - /// - private static int[] getSyntheticData(int size, int startVal, int stopVal) + public static string SaveDataset(List sequences) { - int[] data = new int[size]; - - data = randomRemoveDouble(randomDouble(size, startVal, stopVal), 3); + string basePath = AppDomain.CurrentDomain.BaseDirectory; + string datasetFolder = Path.Combine(basePath, "dataset"); + Directory.CreateDirectory(datasetFolder); // CreateDirectory is safe to call if directory exists + string datasetPath = Path.Combine(datasetFolder, $"dataset_{DateTime.Now.Ticks}.json"); - return data; + Console.WriteLine("Saving dataset..."); + File.WriteAllText(datasetPath, JsonConvert.SerializeObject(sequences)); + return datasetPath; } /// - /// Creates a sorted list of array with given paramerters + /// Creates multiple sequences as per parameters /// - /// Size of array - /// Min range of the list - /// Max range of the list - /// - private static int[] randomDouble(int size, int startVal, int stopVal) + public static List CreateSequences(int count, int size, int startVal, int stopVal) { - int[] array = new int[size]; - List list = new List(); - int number = 0; - Random r = new Random(Guid.NewGuid().GetHashCode()); - while(list.Count < size) - { - number = r.Next(startVal,stopVal); - if (!list.Contains(number)) + return Enumerable.Range(1, count).Select(i => + new Sequence { - if(number >= startVal && number <= stopVal) - list.Add(number); - } - } - - array = list.ToArray(); - Array.Sort(array); - - return array; + name = $"S{i}", + data = GenerateRandomSequence(size, startVal, stopVal) + }) + .ToList(); } - /// - /// Randomly remove less number of items from array - /// - /// array to processed - /// number of removals to be done - /// array with less numbers - private static int[] randomRemoveDouble(int[] array, int less) + private static int[] GenerateRandomSequence(int size, int startVal, int stopVal) { - int[] temp = new int[array.Length - less]; - Random random = new Random(Guid.NewGuid().GetHashCode()); - int number = 0; - List list = new List(); + var rnd = new Random(); + var sequence = new HashSet(); - while (list.Count < (array.Length - less)) + while (sequence.Count < size) { - number = array[random.Next(0, (array.Length))]; - if (!list.Contains(number)) - list.Add(number); + int number = rnd.Next(startVal, stopVal + 1); + sequence.Add(number); } - temp = list.ToArray(); - Array.Sort(temp); - - return temp; - } - - private static int getDigits(int n) - { - if (n >= 0) - { - if (n < 100) return 2; - if (n < 1000) return 3; - if (n < 10000) return 4; - if (n < 100000) return 5; - if (n < 1000000) return 6; - if (n < 10000000) return 7; - if (n < 100000000) return 8; - if (n < 1000000000) return 9; - return 10; - } - else - { - return 2; - } + return sequence.OrderBy(n => n).ToArray(); } } } \ No newline at end of file From f8e5f71af53bd6312ce43efcf031b24d73417406 Mon Sep 17 00:00:00 2001 From: Mahdieh Pirmoradian Date: Mon, 22 Jan 2024 00:37:15 +0100 Subject: [PATCH 14/16] Editing the documentation --- .../docs/ApproveMultiSequenceLearning.md | 289 ++++++++++++++++++ .../docs/images/approve_prediction.png | Bin 0 -> 63138 bytes source/NeoCortexUtils/docs/images/dataset.jpg | Bin 0 -> 52618 bytes .../NeoCortexUtils/docs/images/overview.png | Bin 0 -> 37752 bytes source/NeoCortexUtils/docs/images/result.png | Bin 0 -> 61097 bytes .../NeoCortexUtils/docs/working-with-sdrs.md | 7 +- 6 files changed, 293 insertions(+), 3 deletions(-) create mode 100644 source/NeoCortexUtils/docs/ApproveMultiSequenceLearning.md create mode 100644 source/NeoCortexUtils/docs/images/approve_prediction.png create mode 100644 source/NeoCortexUtils/docs/images/dataset.jpg create mode 100644 source/NeoCortexUtils/docs/images/overview.png create mode 100644 source/NeoCortexUtils/docs/images/result.png diff --git a/source/NeoCortexUtils/docs/ApproveMultiSequenceLearning.md b/source/NeoCortexUtils/docs/ApproveMultiSequenceLearning.md new file mode 100644 index 000000000..7c30a6fc5 --- /dev/null +++ b/source/NeoCortexUtils/docs/ApproveMultiSequenceLearning.md @@ -0,0 +1,289 @@ +# ML22/23-15 Approve Prediction of Multisequence Learning + +## Introduction + +In this project, we have tried to implement new methods along the `MultisequenceLearning` algorithm. The new methods automatically read the dataset from the given path in `HelperMethods.ReadDataset(datasetPath)`, we also have test data in another file which needs to be read for later testing the subsequences in a similar form as `HelperMethods.ReadDataset(testsetPath)`. `RunMultiSequenceLearningExperiment(sequences, sequencesTest)` takes the multiple sequences in `sequences` and test subsequences in `sequencesTest` and is passed to `RunMultiSequenceLearningExperiment(sequences, sequencesTest)`. After learning is completed, calculation of the accuracy of the predicted element. + +## Implementation + +![image](./images/overview.png) + +Fig: Architecture of Approve Prediction of Multisequence Learning + +Above the flow of implementation of our project. + +`Sequence` is the model of how we process and store the dataset. And can be seen below: + +```csharp +public class Sequence +{ + public String name { get; set; } + public int[] data { get; set; } +} +``` + +eg: +- Dataset + +```json +[ + { + "name": "S1", + "data": [ 0, 2, 5, 6, 7, 8, 10, 11, 13 ] + }, + { + "name": "S2", + "data": [ 1, 2, 3, 4, 6, 11, 12, 13, 14 ] + }, + { + "name": "S3", + "data": [ 1, 2, 3, 4, 7, 8, 10, 12, 14 ] + } +] +``` + +- Test Dataset + +```json +[ + { + "name": "T1", + "data": [ 1, 2, 4 ] + }, + { + "name": "T2", + "data": [ 2, 3, 4 ] + }, + { + "name": "T3", + "data": [ 4, 5, 7 ] + }, + { + "name": "T4", + "data": [ 5, 8, 9 ] + } +] + +``` + +Our implemented methods are in `HelperMethod.cs` and can be found [here](../HelperMethods.cs): + +1. FetchHTMConfig() + +Here we save the HTMConfig which is used for Hierarchical Temporal Memory to `Connections` + +```csharp +/// +/// HTM Config for creating Connections +/// +/// input bits +/// number of columns +/// Object of HTMConfig +public static HtmConfig FetchHTMConfig(int inputBits, int numColumns) +{ + HtmConfig cfg = new HtmConfig(new int[] { inputBits }, new int[] { numColumns }) + { + Random = new ThreadSafeRandom(42), + + CellsPerColumn = 25, + GlobalInhibition = true, + LocalAreaDensity = -1, + NumActiveColumnsPerInhArea = 0.02 * numColumns, + PotentialRadius = (int)(0.15 * inputBits), + MaxBoost = 10.0, + DutyCyclePeriod = 25, + MinPctOverlapDutyCycles = 0.75, + MaxSynapsesPerSegment = (int)(0.02 * numColumns), + ActivationThreshold = 15, + ConnectedPermanence = 0.5,e. + PermanenceDecrement = 0.25, + PermanenceIncrement = 0.15, + PredictedSegmentDecrement = 0.1, + }; + + return cfg; +} +``` + +All the fields are self-explanatory as per HTM theory. + +2. getEncoder() + +We have used `ScalarEncoder` since we are encoding all numeric values only. + +Remeber that `inputBits` is same as `HTMConfig`. + +```csharp +/// +/// Get the encoder with settings +/// +/// input bits +/// Object of EncoderBase +public static EncoderBase GetEncoder(int inputBits) +{ + double max = 20; + + Dictionary settings = new Dictionary() + { + { "W", 15}, + { "N", inputBits}, + { "Radius", -1.0}, + { "MinVal", 0.0}, + { "Periodic", false}, + { "Name", "scalar"}, + { "ClipInput", false}, + { "MaxVal", max} + }; + + EncoderBase encoder = new ScalarEncoder(settings); + + return encoder; +} +``` + +Note that `MaxValue` for the encoder is set to `20` which can be changed but then this value should be matched while creating the synthetic dataset. + +3. ReadDataset() + +Reads the JSON file when passed as a full path and returns the object of the list of `Sequence` + +```csharp +/// +/// Reads dataset from the file +/// +/// full path of the file +/// Object of list of Sequence +public static List ReadDataset(string path) +{ + Console.WriteLine("Reading Sequence..."); + String lines = File.ReadAllText(path); + //var sequence = JsonConvert.DeserializeObject(lines); + List sequence = System.Text.Json.JsonSerializer.Deserialize>(lines); + + return sequence; +} +``` + +4. CreateDataset() + +We enhanced to create datasets automatically so we do not have to manually spend time. Here we create a dataset with parameters such as `numberOfSequence` to be created, `size` of a sequence, `startVal` possibly start range, and `endVal` the start range of the sequence. + +```csharp +/// +/// Creates a list of Sequence as per configuration +/// +/// Object of list of Sequence +public static List CreateDataset() +{ + int numberOfSequence = 3; + int size = 12; + int startVal = 0; + int endVal = 15; + Console.WriteLine("Creating Sequence..."); + List sequence = HelperMethods.CreateSequences(numberOfSequence, size, startVal, endVal); + + return sequence; +} +``` + +Note that `endVal` should be less than equal to `MaxVal` of `ScalarEncoder` used above + +5. SaveDataset() + +Saves the dataset in the `dataset` director of the `BasePath` of the application where it is running. + +```csharp +/// +/// Saves the dataset in the 'dataset' folder in BasePath of the application +/// +/// Object of list of Sequence +/// Full path of the dataset +public static string SaveDataset(List sequences) +{ + string BasePath = AppDomain.CurrentDomain.BaseDirectory; + string reportFolder = Path.Combine(BasePath, "dataset"); + if (!Directory.Exists(reportFolder)) + Directory.CreateDirectory(reportFolder); + string reportPath = Path.Combine(reportFolder, $"dataset_{DateTime.Now.Ticks}.json"); + + Console.WriteLine("Saving dataset..."); + + if (!File.Exists(reportPath)) + { + using (StreamWriter sw = File.CreateText(reportPath)) + { + sw.WriteLine(JsonConvert.SerializeObject(sequences)); + } + } + + return reportPath; +} +``` + +6. Calculating accuracy in PredictNextElement() in `Program.cs` + +![image](./images/approve_prediction.png) + +Fig: Predictions and calculating accuracy + +```csharp +int matchCount = 0; +int predictions = 0; +double accuracy = 0.0; + +foreach (var item in list) +{ + Predict(); + //compare current element with prediction of previous element + if(item == Int32.Parse(prediction.Last())) + { + matchCount++; + } + predictions++; + accuracy = (double)matchCount / predictions * 100; +} +``` + +Note that the prediction code is omitted. + +## How to run the project + +### To create a synthetic dataset + +1. Open the [sln](../../../NeoCortexApi.sln) and select `MultiSequenceLearning` as startup project. + +2. In `Program.cs` we have the `Main()`. Uncomment the below code to create a synthetic dataset. + +```csharp +//to create a synthetic dataset +string path = HelperMethods.SaveDataset(HelperMethods.CreateDataset()); +Console.WriteLine($"Dataset saved: {path}"); +``` + +*and comment rest of the lines*. + +3. Run to create the dataset and save the path of the dataset folder and name. + +![dataset](./images/dataset.jpg) + +### To run the experiment + +1. Open the [NeoCortexApi.sln](../../../NeoCortexApi.sln) and select `MultiSequenceLearning` as startup project. + +2. In `Program.cs` we have the `Main()`. Change the name of `dataset` file saved from previous run as seen below: + +```csharp +//to read dataset +string BasePath = AppDomain.CurrentDomain.BaseDirectory; +string datasetPath = Path.Combine(BasePath, "dataset", "dataset_03.json"); //edit name of dataset here +Console.WriteLine($"Reading Dataset: {datasetPath}"); +List sequences = HelperMethods.ReadDataset(datasetPath); +``` + +and also *copy the [test data](../dataset/test_01.json) to the folder* (`{BASEPATH}\neocortexapi\source\MySEProject\MultiSequenceLearning\bin\Debug\net6.0\dataset`). + +## Results + +We have run the experiment max possible number of times with different datasets. 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zjCU9h5=Xl~aed!0Q0Ive_elc3cXR%(^(-Wbu@Aaz?rig2CyP~ Date: Mon, 22 Jan 2024 01:45:34 +0100 Subject: [PATCH 15/16] Update ApproveMultiSequenceLearning.md --- .../docs/ApproveMultiSequenceLearning.md | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/source/NeoCortexUtils/docs/ApproveMultiSequenceLearning.md b/source/NeoCortexUtils/docs/ApproveMultiSequenceLearning.md index 7c30a6fc5..280bb24e4 100644 --- a/source/NeoCortexUtils/docs/ApproveMultiSequenceLearning.md +++ b/source/NeoCortexUtils/docs/ApproveMultiSequenceLearning.md @@ -1,14 +1,13 @@ -# ML22/23-15 Approve Prediction of Multisequence Learning +Approve Prediction of Multisequence Learning ## Introduction - -In this project, we have tried to implement new methods along the `MultisequenceLearning` algorithm. The new methods automatically read the dataset from the given path in `HelperMethods.ReadDataset(datasetPath)`, we also have test data in another file which needs to be read for later testing the subsequences in a similar form as `HelperMethods.ReadDataset(testsetPath)`. `RunMultiSequenceLearningExperiment(sequences, sequencesTest)` takes the multiple sequences in `sequences` and test subsequences in `sequencesTest` and is passed to `RunMultiSequenceLearningExperiment(sequences, sequencesTest)`. After learning is completed, calculation of the accuracy of the predicted element. +In this project, we have tried to implement new methodologies within the MultisequenceLearning algorithm framework. These methods are designed to autonomously load datasets from a specified path using HelperMethods.ReadDataset(datasetPath). Additionally, we have stored test data in a separate file, which is similarly accessed through HelperMethods.ReadDataset(testsetPath) for subsequent testing of subsequences. The function RunMultiSequenceLearningExperiment(sequences, sequencesTest) is employed to process multiple sequences and test subsequences, received respectively as sequences and sequencesTest. Upon completion of the learning phase, the system calculates the accuracy of the predictions made by the algorithm. ## Implementation ![image](./images/overview.png) -Fig: Architecture of Approve Prediction of Multisequence Learning +Fig: Schematic Diagram of Approval Prediction in Multisequence Learning Architecture Above the flow of implementation of our project. @@ -166,7 +165,7 @@ public static List ReadDataset(string path) 4. CreateDataset() -We enhanced to create datasets automatically so we do not have to manually spend time. Here we create a dataset with parameters such as `numberOfSequence` to be created, `size` of a sequence, `startVal` possibly start range, and `endVal` the start range of the sequence. +We have advanced our approach to dataset creation by automating the process, eliminating the need for time-consuming manual intervention. In this improved system, datasets are generated based on specified parameters. These include numberOfSequence, which determines the number of sequences to be created; size, which defines the length of each sequence; and startVal and endVal, which set the starting and ending range values for the sequences, respectively. This streamlined method enhances efficiency and accuracy in dataset generation. ```csharp /// @@ -284,6 +283,6 @@ and also *copy the [test data](../dataset/test_01.json) to the folder* (`{BASEPA ## Results -We have run the experiment max possible number of times with different datasets. We have tried to keep the size of the dataset small and several sequences are also small due to the large time in execution. +We have experimented to the maximum feasible extent, utilizing a variety of datasets. To accommodate the considerable execution time, we deliberately limited the size of the datasets and maintained the sequences at a minimal length. ![results](./images/result.png) From add99c3396c55a14bf397050fd34ec42b34707f7 Mon Sep 17 00:00:00 2001 From: Singari Sahith Kumar Date: Fri, 3 May 2024 17:03:01 +0200 Subject: [PATCH 16/16] Scalar Encoder with Buckets --- .../ML22-23-1 Scalar Encoder with Buckets.pdf | Bin 0 -> 1310954 bytes source/NeoCortexApi/Encoders/EncoderBase.cs | 87 ++- source/NeoCortexApi/Encoders/ScalarEncoder.cs | 581 ++++++++++++++++- .../EncoderTests/ScalarEncoderTests.cs | 586 +++++++++++++++++- 4 files changed, 1210 insertions(+), 44 deletions(-) create mode 100644 source/Documentation/ML22-23-1 Scalar Encoder with Buckets.pdf diff --git a/source/Documentation/ML22-23-1 Scalar Encoder with Buckets.pdf b/source/Documentation/ML22-23-1 Scalar Encoder with Buckets.pdf new file mode 100644 index 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