[GitHub] [incubator-mxnet] xidulu commented on a change in pull request #17302: [numpy]add op random.logistic

[GitBox <gi...@apache.org>]

xidulu commented on a change in pull request #17302: [numpy]add op random.logistic
URL: https://github.com/apache/incubator-mxnet/pull/17302#discussion_r366404838
 
 

 ##########
 File path: src/operator/numpy/random/np_logistic_op.h
 ##########
 @@ -0,0 +1,331 @@
+/*
+ * 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_logistic_op.h
+ * \brief Operator for numpy sampling from logistic distributions
+ */
+#ifndef MXNET_OPERATOR_NUMPY_RANDOM_NP_LOGISTIC_OP_H_
+#define MXNET_OPERATOR_NUMPY_RANDOM_NP_LOGISTIC_OP_H_
+
+#include <mxnet/operator_util.h>
+#include <cstdio>
+#include <algorithm>
+#include <string>
+#include <vector>
+#include <cmath>
+#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 NumpyLogisticParam : public dmlc::Parameter<NumpyLogisticParam> {
+  dmlc::optional<float> loc;
+  dmlc::optional<float> scale;
+  dmlc::optional<mxnet::Tuple<int>> size;
+  DMLC_DECLARE_PARAMETER(NumpyLogisticParam) {
+    DMLC_DECLARE_FIELD(loc);
+    DMLC_DECLARE_FIELD(scale);
+    DMLC_DECLARE_FIELD(size)
+        .set_default(dmlc::optional<mxnet::Tuple<int>>())
+        .describe(
+            "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.");
+  }
+};
+
+inline bool NumpyLogisticOpType(const nnvm::NodeAttrs &attrs,
+                               std::vector<int> *in_attrs,
+                               std::vector<int> *out_attrs) {
+  const NumpyLogisticParam &param = nnvm::get<NumpyLogisticParam>(attrs.parsed);
+  (*out_attrs)[0] = mshadow::kFloat32;
+  (*out_attrs)[1] = mshadow::kFloat32;
+  return true;
+}
+
+namespace mxnet_op {
+
+template <typename DType>
+struct logistic_two_scalar_kernel {
+  MSHADOW_XINLINE static void Map(index_t i, float loc, float scale,
+                                  float *noise, DType *out) {
+    noise[i] = log(noise[i]) - log(1 - noise[i]);
+    out[i] = loc + noise[i] * scale;
+  }
+};
+
+template <typename IType>
+struct check_legal_scale_kernel {
+  MSHADOW_XINLINE static void Map(index_t i, IType *scalar, float* flag) {
+    if (scalar[i] < 0) {
+      *flag = -1.0;
+    }
+  }
+};
+
+template <int ndim, typename IType, typename OType>
+struct logistic_one_scalar_kernel {
+  MSHADOW_XINLINE static void Map(index_t i, int scalar_pos,
+                                  const Shape<ndim> &stride,
+                                  const Shape<ndim> &oshape, IType *array,
+                                  float scalar, float *noise, OType *out) {
+    Shape<ndim> coord = unravel(i, oshape);
+    auto idx = static_cast<index_t>(dot(coord, stride));
+    IType loc_value;
+    IType scale_value;
+    if (scalar_pos == 0) {
+      loc_value = scalar;
+      scale_value = array[idx];
+    } else {
+      loc_value = array[idx];
+      scale_value = scalar;
+    }
+    noise[i] = log(noise[i]) - log(1 - noise[i]);
+    out[i] = loc_value + noise[i] * scale_value;
+  }
+};
+
+template <int ndim, typename IType, typename OType>
+struct logical_kernel {
 
 Review comment:
   Better be `logistic_kernel {...`

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