[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #17316: [NumPy] add op random.laplace
haojin2 commented on a change in pull request #17316: [NumPy] add op
random.laplace
URL: https://github.com/apache/incubator-mxnet/pull/17316#discussion_r366767278
##
File path: src/operator/numpy/random/np_laplace_op.h
##
@@ -0,0 +1,218 @@
+/*
+ * 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_laplace_op.h
+ * \brief Operator for numpy sampling from Laplace distributions
+ */
+#ifndef MXNET_OPERATOR_NUMPY_RANDOM_NP_LAPLACE_OP_H_
+#define MXNET_OPERATOR_NUMPY_RANDOM_NP_LAPLACE_OP_H_
+
+#include
+#include
+#include
+#include
+#include "../../elemwise_op_common.h"
+#include "../../mshadow_op.h"
+#include "../../mxnet_op.h"
+#include "../../operator_common.h"
+#include "../../tensor/elemwise_binary_broadcast_op.h"
+#include "./dist_common.h"
+
+namespace mxnet {
+namespace op {
+
+struct NumpyLaplaceParam : public dmlc::Parameter {
+ dmlc::optional loc;
+ dmlc::optional scale;
+ std::string ctx;
+ int dtype;
+ dmlc::optional> size;
+ DMLC_DECLARE_PARAMETER(NumpyLaplaceParam) {
+DMLC_DECLARE_FIELD(loc);
+DMLC_DECLARE_FIELD(scale);
+DMLC_DECLARE_FIELD(size)
+.set_default(dmlc::optional>())
Review comment:
2-space indentation.
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[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #17316: [NumPy] add op random.laplace
haojin2 commented on a change in pull request #17316: [NumPy] add op
random.laplace
URL: https://github.com/apache/incubator-mxnet/pull/17316#discussion_r366767071
##
File path: python/mxnet/symbol/numpy/random.py
##
@@ -290,6 +290,54 @@ def choice(a, size=None, replace=True, p=None, ctx=None,
out=None):
return _npi.choice(p, a=a, size=size, replace=replace, ctx=ctx,
weighted=True, out=out)
+def laplace(loc=0.0, scale=1.0, size=None, dtype=None, ctx=None, out=None):
+r"""Draw random samples from a Laplace distribution.
+
+Samples are distributed according to a Laplace distribution parametrized
+by *loc* (mean) and *scale* (the exponential decay).
+
+Parameters
+--
+loc : float, The position of the distribution peak.
+
+scale : float, the exponential decay.
+
+size : int or tuple of ints, optional. Output shape. If the given shape
is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in
which case a single value is returned.
+
+dtype : {'float16', 'float32', 'float64'}, optional
+Data type of output samples. Default is 'float32'
+ctx : Context, optional
+Device context of output. Default is current context.
+out : ``ndarray``, optional
+Store output to an existing ``ndarray``.
+
+Returns
+---
+out : _Symbol (symbol representing `mxnet.numpy.ndarray` in computational
graphs)
+Drawn samples from the parameterized Laplace distribution.
+"""
+from ._symbol import _Symbol as np_symbol
+input_type = (isinstance(loc, np_symbol), isinstance(scale, np_symbol))
+if dtype is None:
+dtype = 'float32'
+if ctx is None:
+ctx = current_context()
+if size == ():
+size = None
+if input_type == (True, True):
+return _npi.laplace(loc, scale, loc=None, scale=None, size=size,
+ ctx=ctx, dtype=dtype, out=out)
+elif input_type == (False, True):
+return _npi.laplace(scale, loc=loc, scale=None, size=size,
+ ctx=ctx, dtype=dtype, out=out)
+elif input_type == (True, False):
+return _npi.laplace(loc, loc=None, scale=scale, size=size,
+ ctx=ctx, dtype=dtype, out=out)
Review comment:
alignment
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[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #17316: [NumPy] add op random.laplace
haojin2 commented on a change in pull request #17316: [NumPy] add op
random.laplace
URL: https://github.com/apache/incubator-mxnet/pull/17316#discussion_r366766965
##
File path: python/mxnet/symbol/numpy/random.py
##
@@ -290,6 +290,54 @@ def choice(a, size=None, replace=True, p=None, ctx=None,
out=None):
return _npi.choice(p, a=a, size=size, replace=replace, ctx=ctx,
weighted=True, out=out)
+def laplace(loc=0.0, scale=1.0, size=None, dtype=None, ctx=None, out=None):
+r"""Draw random samples from a Laplace distribution.
+
+Samples are distributed according to a Laplace distribution parametrized
+by *loc* (mean) and *scale* (the exponential decay).
+
+Parameters
+--
+loc : float, The position of the distribution peak.
+
+scale : float, the exponential decay.
+
+size : int or tuple of ints, optional. Output shape. If the given shape
is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in
which case a single value is returned.
+
+dtype : {'float16', 'float32', 'float64'}, optional
+Data type of output samples. Default is 'float32'
+ctx : Context, optional
+Device context of output. Default is current context.
+out : ``ndarray``, optional
+Store output to an existing ``ndarray``.
+
+Returns
+---
+out : _Symbol (symbol representing `mxnet.numpy.ndarray` in computational
graphs)
+Drawn samples from the parameterized Laplace distribution.
+"""
+from ._symbol import _Symbol as np_symbol
+input_type = (isinstance(loc, np_symbol), isinstance(scale, np_symbol))
+if dtype is None:
+dtype = 'float32'
+if ctx is None:
+ctx = current_context()
+if size == ():
+size = None
+if input_type == (True, True):
+return _npi.laplace(loc, scale, loc=None, scale=None, size=size,
+ ctx=ctx, dtype=dtype, out=out)
Review comment:
alignment
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[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #17316: [NumPy] add op random.laplace
haojin2 commented on a change in pull request #17316: [NumPy] add op
random.laplace
URL: https://github.com/apache/incubator-mxnet/pull/17316#discussion_r366767024
##
File path: python/mxnet/symbol/numpy/random.py
##
@@ -290,6 +290,54 @@ def choice(a, size=None, replace=True, p=None, ctx=None,
out=None):
return _npi.choice(p, a=a, size=size, replace=replace, ctx=ctx,
weighted=True, out=out)
+def laplace(loc=0.0, scale=1.0, size=None, dtype=None, ctx=None, out=None):
+r"""Draw random samples from a Laplace distribution.
+
+Samples are distributed according to a Laplace distribution parametrized
+by *loc* (mean) and *scale* (the exponential decay).
+
+Parameters
+--
+loc : float, The position of the distribution peak.
+
+scale : float, the exponential decay.
+
+size : int or tuple of ints, optional. Output shape. If the given shape
is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in
which case a single value is returned.
+
+dtype : {'float16', 'float32', 'float64'}, optional
+Data type of output samples. Default is 'float32'
+ctx : Context, optional
+Device context of output. Default is current context.
+out : ``ndarray``, optional
+Store output to an existing ``ndarray``.
+
+Returns
+---
+out : _Symbol (symbol representing `mxnet.numpy.ndarray` in computational
graphs)
+Drawn samples from the parameterized Laplace distribution.
+"""
+from ._symbol import _Symbol as np_symbol
+input_type = (isinstance(loc, np_symbol), isinstance(scale, np_symbol))
+if dtype is None:
+dtype = 'float32'
+if ctx is None:
+ctx = current_context()
+if size == ():
+size = None
+if input_type == (True, True):
+return _npi.laplace(loc, scale, loc=None, scale=None, size=size,
+ ctx=ctx, dtype=dtype, out=out)
+elif input_type == (False, True):
+return _npi.laplace(scale, loc=loc, scale=None, size=size,
+ ctx=ctx, dtype=dtype, out=out)
Review comment:
alignment
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[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #17316: [NumPy] add op random.laplace
haojin2 commented on a change in pull request #17316: [NumPy] add op
random.laplace
URL: https://github.com/apache/incubator-mxnet/pull/17316#discussion_r366767094
##
File path: python/mxnet/symbol/numpy/random.py
##
@@ -290,6 +290,54 @@ def choice(a, size=None, replace=True, p=None, ctx=None,
out=None):
return _npi.choice(p, a=a, size=size, replace=replace, ctx=ctx,
weighted=True, out=out)
+def laplace(loc=0.0, scale=1.0, size=None, dtype=None, ctx=None, out=None):
+r"""Draw random samples from a Laplace distribution.
+
+Samples are distributed according to a Laplace distribution parametrized
+by *loc* (mean) and *scale* (the exponential decay).
+
+Parameters
+--
+loc : float, The position of the distribution peak.
+
+scale : float, the exponential decay.
+
+size : int or tuple of ints, optional. Output shape. If the given shape
is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in
which case a single value is returned.
+
+dtype : {'float16', 'float32', 'float64'}, optional
+Data type of output samples. Default is 'float32'
+ctx : Context, optional
+Device context of output. Default is current context.
+out : ``ndarray``, optional
+Store output to an existing ``ndarray``.
+
+Returns
+---
+out : _Symbol (symbol representing `mxnet.numpy.ndarray` in computational
graphs)
+Drawn samples from the parameterized Laplace distribution.
+"""
+from ._symbol import _Symbol as np_symbol
+input_type = (isinstance(loc, np_symbol), isinstance(scale, np_symbol))
+if dtype is None:
+dtype = 'float32'
+if ctx is None:
+ctx = current_context()
+if size == ():
+size = None
+if input_type == (True, True):
+return _npi.laplace(loc, scale, loc=None, scale=None, size=size,
+ ctx=ctx, dtype=dtype, out=out)
+elif input_type == (False, True):
+return _npi.laplace(scale, loc=loc, scale=None, size=size,
+ ctx=ctx, dtype=dtype, out=out)
+elif input_type == (True, False):
+return _npi.laplace(loc, loc=None, scale=scale, size=size,
+ ctx=ctx, dtype=dtype, out=out)
+else:
+return _npi.laplace(loc=loc, scale=scale, size=size,
+ ctx=ctx, dtype=dtype, out=out)
Review comment:
alignment
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