[numpy] add numpy operator : append

python/mxnet/ndarray/numpy/_op.py

@@ -33,7 +33,7 @@
            'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2',
            'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot', 'histogram', 'eye',
-           'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate',
+           'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate', 'append',
            'stack', 'vstack', 'column_stack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax',
            'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
            'around', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
@@ -2919,8 +2919,64 @@ def concatenate(seq, axis=0, out=None):
     -------
     res : ndarray
         The concatenated array.
+
+    Examples
+    --------
+    >>> a = np.array([[1, 2], [3, 4]])
+    >>> b = np.array([[5, 6]])
+    >>> np.concatenate((a, b), axis=0)
+    array([[1., 2.],
+           [3., 4.],
+           [5., 6.]])
+
+    >>> np.concatenate((a, b), axis=None)
+    array([1., 2., 3., 4., 5., 6.])
+
+    >>> np.concatenate((a, b.T), axis=1)
+    array([[1., 2., 5.],
+           [3., 4., 6.]])
+    """
+    return _npi.concatenate(*seq, axis=axis, out=out)
+
+
+@set_module('mxnet.ndarray.numpy')
+def append(arr, values, axis=None):  # pylint: disable=redefined-outer-name
+    """
+    Append values to the end of an array.
+
+    Parameters
+    ----------
+    arr : ndarray
+        Values are appended to a copy of this array.
+    values : ndarray
+        These values are appended to a copy of `arr`.  It must be of the
+        correct shape (the same shape as `arr`, excluding `axis`).  If
+        `axis` is not specified, `values` can be any shape and will be
+        flattened before use.
+    axis : int, optional
+        The axis along which `values` are appended.  If `axis` is not
+        given, both `arr` and `values` are flattened before use.
+
+    Returns
+    -------
+    append : ndarray
+        A copy of `arr` with `values` appended to `axis`.  Note that
+        `append` does not occur in-place: a new array is allocated and
+        filled.  If `axis` is None, `out` is a flattened array.
+
+    Examples
+    --------
+    >>> np.append(np.array([1, 2, 3]), np.array([[4, 5, 6],[7, 8, 9]]))
+    array([1., 2., 3., 4., 5., 6., 7., 8., 9.])
+
+    When `axis` is specified, `values` must have the correct shape.
+
+    >>> np.append(np.array([[1, 2, 3], [4, 5, 6]]), np.array([[7, 8, 9]]), axis=0)
+    array([[1., 2., 3.],
+           [4., 5., 6.],
+           [7., 8., 9.]])
     """
-    return _npi.concatenate(*seq, dim=axis, out=out)
+    return _npi.concatenate(arr, values, axis=axis, out=None)
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -5014,7 +5070,7 @@ def may_share_memory(a, b, max_work=None):
     return _npi.share_memory(a, b).item()
 
 
-def diff(a, n=1, axis=-1, prepend=None, append=None):
+def diff(a, n=1, axis=-1, prepend=None, append=None):  # pylint: disable=redefined-outer-name
     r"""
     numpy.diff(a, n=1, axis=-1, prepend=<no value>, append=<no value>)
 

python/mxnet/numpy/multiarray.py

@@ -49,7 +49,7 @@
            'mod', 'remainder', 'power', 'arctan2', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10',
            'sqrt', 'cbrt', 'abs', 'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log',
            'degrees', 'log2', 'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative',
-           'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh',
+           'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'append',
            'tensordot', 'histogram', 'eye', 'linspace', 'logspace', 'expand_dims', 'tile', 'arange',
            'split', 'vsplit', 'concatenate', 'stack', 'vstack', 'column_stack', 'dstack', 'mean', 'maximum', 'minimum',
            'swapaxes', 'clip', 'argmax', 'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming',
@@ -4803,10 +4803,53 @@ def concatenate(seq, axis=0, out=None):
     >>> np.concatenate((a, b.T), axis=1)
     array([[1., 2., 5.],
            [3., 4., 6.]])
+
+    >>> np.concatenate((a, b), axis=None)
+    array([1., 2., 3., 4., 5., 6.])
     """
     return _mx_nd_np.concatenate(seq, axis=axis, out=out)
 
 
+@set_module('mxnet.numpy')
+def append(arr, values, axis=None):  # pylint: disable=redefined-outer-name
+    """
+    Append values to the end of an array.
+
+    Parameters
+    ----------
+    arr : ndarray
+        Values are appended to a copy of this array.
+    values : ndarray
+        These values are appended to a copy of `arr`.  It must be of the
+        correct shape (the same shape as `arr`, excluding `axis`).  If
+        `axis` is not specified, `values` can be any shape and will be
+        flattened before use.
+    axis : int, optional
+        The axis along which `values` are appended.  If `axis` is not
+        given, both `arr` and `values` are flattened before use.
+
+    Returns
+    -------
+    append : ndarray
+        A copy of `arr` with `values` appended to `axis`.  Note that
+        `append` does not occur in-place: a new array is allocated and
+        filled.  If `axis` is None, `out` is a flattened array.
+
+    Examples
+    --------
+    >>> np.append(np.array([1, 2, 3]), np.array([[4, 5, 6],[7, 8, 9]]))
+    array([1., 2., 3., 4., 5., 6., 7., 8., 9.])
+
+    When `axis` is specified, `values` must have the correct shape.
+
+    >>> np.append(np.array([[1, 2, 3], [4, 5, 6]]), np.array([[7, 8, 9]]), axis=0)
+    array([[1., 2., 3.],
+           [4., 5., 6.],
+           [7., 8., 9.]])
+    """
+    return _mx_nd_np.append(arr, values, axis=axis)
+
+
 @set_module('mxnet.numpy')
 def stack(arrays, axis=0, out=None):
     """Join a sequence of arrays along a new axis.
@@ -7018,7 +7061,7 @@ def may_share_memory(a, b, max_work=None):
     return _mx_nd_np.may_share_memory(a, b, max_work)
 
 
-def diff(a, n=1, axis=-1, prepend=None, append=None):
+def diff(a, n=1, axis=-1, prepend=None, append=None):  # pylint: disable=redefined-outer-name
     r"""
     numpy.diff(a, n=1, axis=-1, prepend=<no value>, append=<no value>)
 

python/mxnet/numpy_dispatch_protocol.py

@@ -86,6 +86,7 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'argmin',
     'argmax',
     'around',
+    'append',
     'broadcast_arrays',
     'broadcast_to',
     'clip',

python/mxnet/symbol/numpy/_symbol.py

@@ -35,7 +35,7 @@
            'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2', 'log1p',
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot', 'histogram', 'eye',
-           'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate',
+           'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate', 'append',
            'stack', 'vstack', 'column_stack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax',
            'argmin', 'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
            'around', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
@@ -2992,6 +2992,7 @@ def vsplit(ary, indices_or_sections):
 @set_module('mxnet.symbol.numpy')
 def concatenate(seq, axis=0, out=None):
     """Join a sequence of arrays along an existing axis.
+
     Parameters
     ----------
     a1, a2, ... : sequence of array_like
@@ -3004,12 +3005,69 @@ def concatenate(seq, axis=0, out=None):
         If provided, the destination to place the result. The shape must be
         correct, matching that of what concatenate would have returned if no
         out argument were specified.
+
     Returns
     -------
     res : ndarray
         The concatenated array.
+
+    Examples
+    --------
+    >>> a = np.array([[1, 2], [3, 4]])
+    >>> b = np.array([[5, 6]])
+    >>> np.concatenate((a, b), axis=0)
+    array([[1., 2.],
+           [3., 4.],
+           [5., 6.]])
+
+    >>> np.concatenate((a, b), axis=None)
+    array([1., 2., 3., 4., 5., 6.])
+
+    >>> np.concatenate((a, b.T), axis=1)
+    array([[1., 2., 5.],
+           [3., 4., 6.]])
+    """
+    return _npi.concatenate(*seq, axis=axis, out=out)
+
+
+@set_module('mxnet.symbol.numpy')
+def append(arr, values, axis=None):  # pylint: disable=redefined-outer-name
+    """
+    Append values to the end of an array.
+
+    Parameters
+    ----------
+    arr : ndarray
+        Values are appended to a copy of this array.
+    values : ndarray
+        These values are appended to a copy of `arr`.  It must be of the
+        correct shape (the same shape as `arr`, excluding `axis`).  If
+        `axis` is not specified, `values` can be any shape and will be
+        flattened before use.
+    axis : int, optional
+        The axis along which `values` are appended.  If `axis` is not
+        given, both `arr` and `values` are flattened before use.
+
+    Returns
+    -------
+    append : ndarray
+        A copy of `arr` with `values` appended to `axis`.  Note that
+        `append` does not occur in-place: a new array is allocated and
+        filled.  If `axis` is None, `out` is a flattened array.
+
+    Examples
+    --------
+    >>> np.append(np.array([1, 2, 3]), np.array([[4, 5, 6],[7, 8, 9]]))
+    array([1., 2., 3., 4., 5., 6., 7., 8., 9.])
+
+    When `axis` is specified, `values` must have the correct shape.
+
+    >>> np.append(np.array([[1, 2, 3], [4, 5, 6]]), np.array([[7, 8, 9]]), axis=0)
+    array([[1., 2., 3.],
+           [4., 5., 6.],
+           [7., 8., 9.]])
     """
-    return _npi.concatenate(*seq, dim=axis, out=out)
+    return _npi.concatenate(arr, values, axis=axis, out=None)
 
 
 @set_module('mxnet.symbol.numpy')
@@ -4665,7 +4723,7 @@ def may_share_memory(a, b, max_work=None):
     return _npi.share_memory(a, b)
 
 
-def diff(a, n=1, axis=-1, prepend=None, append=None):
+def diff(a, n=1, axis=-1, prepend=None, append=None):  # pylint: disable=redefined-outer-name
     r"""
     numpy.diff(a, n=1, axis=-1, prepend=<no value>, append=<no value>)
 

src/operator/numpy/np_matrix_op-inl.h

@@ -864,6 +864,87 @@ inline void HSplitOpBackward(const nnvm::NodeAttrs &attrs,
   }
   SplitOpBackwardImpl<xpu>(attrs, ctx, inputs, req, outputs, real_axis);
 }
+
+struct NumpyConcatenateParam : public dmlc::Parameter<NumpyConcatenateParam> {
+  int num_args;
+  dmlc::optional<int> axis;
+  DMLC_DECLARE_PARAMETER(NumpyConcatenateParam) {
+    DMLC_DECLARE_FIELD(num_args)
+    .set_lower_bound(1)
+    .describe("Number of inputs to be concated.");
+    DMLC_DECLARE_FIELD(axis)
+    .set_default(dmlc::optional<int>(0))
+    .describe("The axis along which `values` are appended.  If `axis` is not"
+              "given, both `arr` and `values` are flattened before use.");
+  }
+};
+
+template<typename xpu>
+void NumpyConcatenateForward(const nnvm::NodeAttrs& attrs,
+                             const OpContext& ctx,
+                             const std::vector<TBlob>& inputs,
+                             const std::vector<OpReqType>& req,
+                             const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mshadow_op;
+
+  const NumpyConcatenateParam& param = nnvm::get<NumpyConcatenateParam>(attrs.parsed);
+  CHECK_EQ(inputs.size(), param.num_args);
+  CHECK_EQ(outputs.size(), 1U);
+  CHECK_EQ(req.size(), 1U);
+
+  std::vector<TBlob> data(param.num_args);
+  for (int i = 0; i < param.num_args; i++) {
+    if (!param.axis.has_value()) {
+      data[i] = inputs[i].reshape(Shape1(inputs[i].shape_.Size()));
+    } else {
+      data[i] = inputs[i];
+    }
+  }
+
+  ConcatParam cparam;
+  cparam.num_args = param.num_args;
+  cparam.dim = param.axis.has_value() ? param.axis.value() : 0;
+  MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    ConcatOp<xpu, DType> op;
+    op.Init(cparam);
+    op.Forward(ctx, data, req, outputs);
+  });
+}
+
+template<typename xpu>
+void NumpyConcatenateBackward(const nnvm::NodeAttrs& attrs,
+                              const OpContext& ctx,
+                              const std::vector<TBlob>& inputs,
+                              const std::vector<OpReqType>& req,
+                              const std::vector<TBlob>& outputs) {
+  using namespace mshadow;
+  using namespace mshadow_op;
+
+  const NumpyConcatenateParam& param = nnvm::get<NumpyConcatenateParam>(attrs.parsed);
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), param.num_args);
+  CHECK_EQ(req.size(), param.num_args);
+
+  std::vector<TBlob> data(param.num_args);
+  for (int i = 0; i < param.num_args; i++) {
+    if (!param.axis.has_value()) {
+      data[i] = outputs[i].reshape(Shape1(outputs[i].shape_.Size()));
+    } else {
+      data[i] = outputs[i];
+    }
+  }
+
+  ConcatParam cparam;
+  cparam.num_args = param.num_args;
+  cparam.dim = param.axis.has_value() ? param.axis.value() : 0;
+  MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    ConcatOp<xpu, DType> op;
+    op.Init(cparam);
+    op.Backward(ctx, inputs[0], req, data);
+  });
+}
+
 }  // namespace op
 }  // namespace mxnet