numpy bincount

python/mxnet/ndarray/numpy/_op.py

@@ -40,7 +40,7 @@
            'blackman', 'flip', 'around', 'hypot', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad', 'unique', 'lcm',
            'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less',
            'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory',
-           'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where']
+           'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where', 'bincount']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -5864,3 +5864,60 @@ def where(condition, x=None, y=None):
         return nonzero(condition)
     else:
         return _npi.where(condition, x, y, out=None)
+
+
+@set_module('mxnet.ndarray.numpy')
+def bincount(x, weights=None, minlength=0):
+    """
+    Count number of occurrences of each value in array of non-negative ints.
+
+    Parameters
+    ----------
+    x : ndarray
+        input array, 1 dimension, nonnegative ints.
+    weights: ndarray
+        input weigths same shape as x. (Optional)
+    minlength: int
+        A minimum number of bins for the output. (Optional)
+
+    Returns
+    --------
+    out : ndarray
+        the result of binning the input array. The length of out is equal to amax(x)+1.
+
+    Raises
+    --------
+    Value Error
+        If the input is not 1-dimensional, or contains elements with negative values,
+        or if minlength is negative
+    TypeError
+        If the type of the input is float or complex.
+
+    Examples
+    --------
+    >>> np.bincount(np.arange(5))
+    array([1, 1, 1, 1, 1])
+    >>> np.bincount(np.array([0, 1, 1, 3, 2, 1, 7]))
+    array([1, 3, 1, 1, 0, 0, 0, 1])
+
+    >>> x = np.array([0, 1, 1, 3, 2, 1, 7, 23])
+    >>> np.bincount(x).size == np.amax(x)+1
+    True
+
+    >>> np.bincount(np.arange(5, dtype=float))
+    Traceback (most recent call last):
+    File "<stdin>", line 1, in <module>
+    TypeError: array cannot be safely cast to required type
+
+    >>> w = np.array([0.3, 0.5, 0.2, 0.7, 1., -0.6]) # weights
+    >>> x = np.array([0, 1, 1, 2, 2, 2])
+    >>> np.bincount(x,  weights=w)
+    array([ 0.3,  0.7,  1.1])
+    """
+    if not isinstance(x, NDArray):
+        raise TypeError("Input data should be NDarray")
+    if minlength < 0:
+        raise ValueError("Minlength value should greater than 0")
+    if weights is None:
+        return _npi.bincount(x, minlength=minlength, has_weights=False)
+    return _npi.bincount(x, weights=weights, minlength=minlength, has_weights=True)

python/mxnet/numpy/multiarray.py

@@ -59,7 +59,7 @@
            'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
            'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal',
            'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory',
-           'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where']
+           'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where', 'bincount']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -7840,3 +7840,54 @@ def where(condition, x=None, y=None):
            [ 0.,  3., -1.]])
     """
     return _mx_nd_np.where(condition, x, y)
+
+
+@set_module('mxnet.numpy')
+def bincount(x, weights=None, minlength=0):
+    """
+    Count number of occurrences of each value in array of non-negative ints.
+
+    Parameters
+    ----------
+    x : ndarray
+        input array, 1 dimension, nonnegative ints.
+    weights: ndarray
+        input weigths same shape as x. (Optional)
+    minlength: int
+        A minimum number of bins for the output. (Optional)
+
+    Returns
+    --------
+    out : ndarray
+        the result of binning the input array. The length of out is equal to amax(x)+1.
+
+    Raises
+    --------
+    Value Error
+        If the input is not 1-dimensional, or contains elements with negative values,
+        or if minlength is negative
+    TypeError
+        If the type of the input is float or complex.
+
+    Examples
+    --------
+    >>> np.bincount(np.arange(5))
+    array([1, 1, 1, 1, 1])
+    >>> np.bincount(np.array([0, 1, 1, 3, 2, 1, 7]))
+    array([1, 3, 1, 1, 0, 0, 0, 1])
+
+    >>> x = np.array([0, 1, 1, 3, 2, 1, 7, 23])
+    >>> np.bincount(x).size == np.amax(x)+1
+    True
+
+    >>> np.bincount(np.arange(5, dtype=float))
+    Traceback (most recent call last):
+    File "<stdin>", line 1, in <module>
+    TypeError: array cannot be safely cast to required type
+
+    >>> w = np.array([0.3, 0.5, 0.2, 0.7, 1., -0.6]) # weights
+    >>> x = np.array([0, 1, 1, 2, 2, 2])
+    >>> np.bincount(x,  weights=w)
+    array([ 0.3,  0.7,  1.1])
+    """
+    return _mx_nd_np.bincount(x, weights=weights, minlength=minlength)

python/mxnet/numpy_dispatch_protocol.py

@@ -145,6 +145,7 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'resize',
     'where',
     'full_like',
+    'bincount'
 ]
 
 

python/mxnet/symbol/numpy/_symbol.py

@@ -48,7 +48,7 @@
            'blackman', 'flip', 'around', 'hypot', 'bitwise_xor', 'bitwise_or', 'rad2deg', 'deg2rad', 'unique', 'lcm',
            'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less',
            'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'shares_memory',
-           'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where']
+           'may_share_memory', 'diff', 'resize', 'nan_to_num', 'where', 'bincount']
 
 
 @set_module('mxnet.symbol.numpy')
@@ -5420,4 +5420,38 @@ def load_json(json_str):
     return _Symbol(handle)
 
 
+@set_module('mxnet.symbol.numpy')
+def bincount(x, weights=None, minlength=0):
+    """
+    Count number of occurrences of each value in array of non-negative ints.
+
+    Parameters
+    ----------
+    x : _Symbol
+        input data
+    weights: _Symbol
+        input weigths same shape as x. (Optional)
+    minlength: int
+        A minimum number of bins for the output. (Optional)
+
+    Returns
+    --------
+    out : _Symbol
+        the result of binning the input data. The length of out is equal to amax(x)+1.
+
+    Raises:
+    --------
+    Value Error
+        If the input is not 1-dimensional, or contains elements with negative values,
+        or if minlength is negative
+    TypeError
+        If the type of the input is float or complex.
+    """
+    if minlength < 0:
+        raise ValueError("Minlength value should greater than 0")
+    if weights is None:
+        return _npi.bincount(x, minlength=minlength, has_weights=False)
+    return _npi.bincount(x, weights=weights, minlength=minlength, has_weights=True)
+
+
 _set_np_symbol_class(_Symbol)

src/operator/numpy/np_bincount_op-inl.h

@@ -0,0 +1,147 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file np_bicount_op-inl.h
+ * \brief numpy compatible bincount operator
+ */
+#ifndef MXNET_OPERATOR_NUMPY_NP_BINCOUNT_OP_INL_H_
+#define MXNET_OPERATOR_NUMPY_NP_BINCOUNT_OP_INL_H_
+
+#include <mxnet/operator_util.h>
+#include <utility>
+#include <vector>
+#include "../mshadow_op.h"
+#include "../mxnet_op.h"
+#include "../operator_common.h"
+#include "../elemwise_op_common.h"
+#include "np_broadcast_reduce_op.h"
+
+namespace mxnet {
+namespace op {
+
+struct NumpyBincountParam : public dmlc::Parameter<NumpyBincountParam> {
+  int minlength;
+  bool has_weights;
+  DMLC_DECLARE_PARAMETER(NumpyBincountParam) {
+    DMLC_DECLARE_FIELD(minlength)
+    .set_default(0)
+    .describe("A minimum number of bins for the output array"
+              "If minlength is specified, there will be at least this"
+              "number of bins in the output array");
+    DMLC_DECLARE_FIELD(has_weights)
+    .set_default(false)
+    .describe("Determine whether Bincount has weights.");
+  }
+};
+
+inline bool NumpyBincountType(const nnvm::NodeAttrs& attrs,
+                              std::vector<int> *in_attrs,
+                              std::vector<int> *out_attrs) {
+  const NumpyBincountParam& param = nnvm::get<NumpyBincountParam>(attrs.parsed);
+  if (!param.has_weights) {
+    return ElemwiseType<1, 1>(attrs, in_attrs, out_attrs) && in_attrs->at(0) != -1;
+  } else {
+    CHECK_EQ(out_attrs->size(), 1U);
+    CHECK_EQ(in_attrs->size(), 2U);
+    TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(1));
+    TYPE_ASSIGN_CHECK(*in_attrs, 1, out_attrs->at(0));
+    return out_attrs->at(0) != -1 && in_attrs->at(0) != -1;
+  }
+}
+
+inline bool NumpyBincountStorageType(const nnvm::NodeAttrs& attrs,
+                                     const int dev_mask,
+                                     DispatchMode* dispatch_mode,
+                                     std::vector<int> *in_attrs,
+                                     std::vector<int> *out_attrs) {
+  const NumpyBincountParam& param = nnvm::get<NumpyBincountParam>(attrs.parsed);
+  if (param.has_weights) {
+    CHECK_EQ(in_attrs->size(), 2U);
+  } else {
+    CHECK_EQ(in_attrs->size(), 1U);
+  }
+  CHECK_EQ(out_attrs->size(), 1U);
+  for (int &attr : *in_attrs) {
+    CHECK_EQ(attr, kDefaultStorage) << "Only default storage is supported";
+  }
+  for (int &attr : *out_attrs) {
+    attr = kDefaultStorage;
+  }
+  *dispatch_mode = DispatchMode::kFComputeEx;
+  return true;
+}
+
+template<typename xpu>
+void NumpyBincountForwardImpl(const OpContext &ctx,
+                              const NDArray &data,
+                              const NDArray &weights,
+                              const NDArray &out,
+                              const size_t &data_n,
+                              const int &minlength);
+
+template<typename xpu>
+void NumpyBincountForwardImpl(const OpContext &ctx,
+                              const NDArray &data,
+                              const NDArray &out,
+                              const size_t &data_n,
+                              const int &minlength);
+
+template<typename xpu>
+void NumpyBincountForward(const nnvm::NodeAttrs& attrs,
+                          const OpContext &ctx,
+                          const std::vector<NDArray> &inputs,
+                          const std::vector<OpReqType> &req,
+                          const std::vector<NDArray> &outputs) {
+  CHECK_GE(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 1U);
+  CHECK(req[0] == kWriteTo);
+  const NumpyBincountParam& param = nnvm::get<NumpyBincountParam>(attrs.parsed);
+  const bool has_weights = param.has_weights;
+  const int minlength = param.minlength;
+  const NDArray &data = inputs[0];
+  const NDArray &out = outputs[0];
+  CHECK_LE(data.shape().ndim(), 1U) << "Input only accept 1d array";
+  CHECK(!common::is_float(data.dtype())) <<"Input data should be int type";
+  size_t N = data.shape().Size();
+  if (N == 0) {
+    mshadow::Stream<xpu> *stream = ctx.get_stream<xpu>();
+    mxnet::TShape s(1, minlength);
+    const_cast<NDArray &>(out).Init(s);
+    MSHADOW_TYPE_SWITCH(out.dtype(), OType, {
+      mxnet_op::Kernel<mxnet_op::set_zero, xpu>::Launch(
+        stream, minlength, out.data().dptr<OType>());
+    });
+  } else {
+    if (has_weights) {
+      CHECK_EQ(inputs.size(), 2U);
+      const NDArray &weights = inputs[1];
+      CHECK_EQ(data.shape(), weights.shape()) << "weights should has same size as input";
+      NumpyBincountForwardImpl<xpu>(ctx, data, weights, out, N, minlength);
+    } else {
+      NumpyBincountForwardImpl<xpu>(ctx, data, out, N, minlength);
+    }
+  }
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_NUMPY_NP_BINCOUNT_OP_INL_H_