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KritiCXLogicDeducer.py
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KritiCXLogicDeducer.py
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import numpy as np
def input_locations(matrix2x2):
input_matrix = matrix2x2[:,:2]
types_matrix = (input_matrix/10).astype(int)
return (types_matrix == 8) | (types_matrix == 9) | (types_matrix == 10)
def unique_init_inputs(matrix2x2):
input_loc = input_locations(matrix2x2)
total_unique_inputs = np.any((matrix2x2/10).astype(int) == 8).astype(int) + np.any((matrix2x2/10).astype(int) == 9).astype(int) + np.any((matrix2x2/10).astype(int) == 10).astype(int)
mask_red = (matrix2x2/10).astype(int) == 8
mask_green = (matrix2x2/10).astype(int) == 9
mask_blue = (matrix2x2/10).astype(int) == 10
return total_unique_inputs, [mask_red,mask_green,mask_blue], [np.any(mask_red),np.any(mask_green),np.any(mask_blue)]
def all_possible_inp_maker(matrix2x2):
total_inps,_,_ = unique_init_inputs(matrix2x2) #This was to find the no. of all the inputs which WAS WRONG, Now it's correct...
arr = np.zeros(total_inps)
all_bin_list = []
for i in range(np.power(2,total_inps)):
bin_string = bin(i)[2:].rjust(total_inps,'0')
all_bin_list.append([i for i in bin_string])
return np.array(all_bin_list).astype(int)
def input_value_putter(matrix2x2,vector_to_put):
unique_inputs, masks, inp_types = unique_init_inputs(matrix2x2)
new_matrix = np.zeros_like(matrix2x2)
for i in range(unique_inputs):
new_matrix[masks[i]] = vector_to_put[i]
return new_matrix,inp_types
def output_generator_4_inputs(matrix2x2, binary_vector):
#Where's binary_vector???? Tick
#Initial Evaluation directly from inputs
matrix_4_op,inp_types = input_value_putter(matrix2x2,binary_vector)
out_mat = matrix_4_op
input_loc = input_locations(matrix2x2)
cache = np.zeros_like(input_loc).astype(bool)
#INITIALIZE THE INPUT FOR THE NOT GATES
for i,val in np.ndenumerate(matrix2x2[:,2]):
if int(val/10) == 7:
if input_loc[i,0] == True:
input_loc[i,1] = True
for i in range(matrix_4_op.shape[0]):
if input_loc[i,0]:
if int(matrix2x2[i,2]/10) == 1:
out_mat[i,2] = out_mat[i,0] and out_mat[i,1]
elif int(matrix2x2[i,2]/10) == 2:
out_mat[i,2] = out_mat[i,0] or out_mat[i,1]
elif int(matrix2x2[i,2]/10) == 3:
out_mat[i,2] = not(out_mat[i,0] and out_mat[i,1])
elif int(matrix2x2[i,2]/10) == 4:
out_mat[i,2] = not(out_mat[i,0] or out_mat[i,1])
elif int(matrix2x2[i,2]/10) == 5:
out_mat[i,2] = out_mat[i,0] ^ out_mat[i,1]
elif int(matrix2x2[i,2]/10) == 6:
out_mat[i,2] = not(out_mat[i,0] ^ out_mat[i,1])
elif int(matrix2x2[i,2]/10) == 7:
out_mat[i,2] = not(out_mat[i,0])
#checking 111, i.e output
#elif int(matrix_4_op[i,2]) == 111:
# output = matrix_4_op[i,2]
count = 0
#print(input_loc,out_mat)
while True:
count+=1
#PUT OUTPUT TO NEXT INPUTS
#inserting the values just calculated, in out_mat to the specefic locations in the two input columns::--
for i,outs in np.ndenumerate(matrix2x2[:,2]):
if input_loc[i,0] & input_loc[i,1]:
out_mat[:,:2][matrix2x2[:,:2] == outs] = out_mat[:,2][matrix2x2[:,2] == outs]
#Check the fucking pairs and initialize the input_loc again
#SIMILARILY, UPDATE THE input_loc BOOL TABLE
pairs_loc = input_loc[:,0] & input_loc[:,1]
#Long Term Memory
input_loc = np.zeros_like(input_loc).astype(bool)
for i in range(cache.shape[0]):
if (((cache[i,0] ==True) & (cache[i,1]==False))) :#| ((cache[i,0] ==True) & (cache[i,1]==False))):
input_loc[i,0] = True
elif (((cache[i,0] ==False) & (cache[i,1]==True))):
input_loc[i,1] = True
for i,val in np.ndenumerate(pairs_loc):
if val:
input_loc[matrix2x2[:,:2] == matrix2x2[i,2]] = True
#Checks whether the next input is for NOT gate, AND IF IT IS, PUT A TRUE ON IT'S NEXT ROW TOO (I know it's an inefficient implementation!!)
for i,val in np.ndenumerate(matrix2x2[:,2]):
if int(val/10) == 7:
if input_loc[i,0] == True:
input_loc[i,1] = True
#LOOP BREAKING CONDITION
#Below Condition is for the breaking from the loop when the corresponding row in which the flow is have output as '111'
if np.any(matrix2x2[input_loc[:,0] == True][:,2] == 111):
output = out_mat[:,0][matrix2x2[:,2] == 111]
break
#CALCULATE NEXT OUTPUTS
#Now, the new pairs are made (if there's any) and now we need to calculate the new outputs to put in out_mat
for i in range(matrix_4_op.shape[0]):
if input_loc[i,0] & input_loc[i,1]:
if int(matrix2x2[i,2]/10) == 1:
out_mat[i,2] = out_mat[i,0] and out_mat[i,1]
elif int(matrix2x2[i,2]/10) == 2:
out_mat[i,2] = out_mat[i,0] or out_mat[i,1]
elif int(matrix2x2[i,2]/10) == 3:
out_mat[i,2] = not(out_mat[i,0] and out_mat[i,1])
elif int(matrix2x2[i,2]/10) == 4:
out_mat[i,2] = not(out_mat[i,0] or out_mat[i,1])
elif int(matrix2x2[i,2]/10) == 5:
out_mat[i,2] = out_mat[i,0] ^ out_mat[i,1]
elif int(matrix2x2[i,2]/10) == 6:
out_mat[i,2] = not(out_mat[i,0] ^ out_mat[i,1])
elif int(matrix2x2[i,2]/10) == 7:
out_mat[i,2] = not(out_mat[i,0])
#verb_count+=1
#print('While Loop number {}'.format(verb_count))
#print(input_loc,out_mat)
if count==1:
cache = input_loc
return output,out_mat,input_loc,inp_types
#After this the input_pair_matrix[:,:2] will be filled by 1's where the pairs are there.
#After this the input_pair_matrix[:,:2] will be filled by 1's where the pairs are there.
def truth_table_generator(matrix2x2):
binary_vectorS = all_possible_inp_maker(matrix2x2)
TT = []
for i in range(binary_vectorS.shape[0]):
output, _ , _ , inp_types= output_generator_4_inputs(matrix2x2,binary_vectorS[i])
TT.append([binary_vectorS[i],output])
print("Outputs:")
l = []
for i in range(3):
if ((inp_types[i] == True) and (i==0)):
l.append("Red")
elif ((inp_types[i] == True) and (i==1)):
l.append("Green")
elif ((inp_types[i] == True) and (i==2)):
l.append("Blue")
print(l)
return np.array(TT)