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/*
* 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.
*/
package ml.dmlc.mxnet
import ml.dmlc.mxnet.Base._
/**
* Random Number interface of mxnet.
*/
object Random {
/**
* Generate uniform distribution in [low, high) with shape.
*
* @param low The lower bound of distribution.
* @param high The upper bound of distribution.
* @param shape Output shape of the NDArray generated.
* @param ctx Context of output NDArray, will use default context if not specified.
* @param out Output place holder
* @return The result NDArray with generated result.
*/
def uniform(low: Float,
high: Float,
shape: Shape = null,
ctx: Context = null,
out: NDArray = null): NDArray = {
var outCopy = out
if (outCopy != null) {
require(shape == null && ctx == null, "shape and ctx is not needed when out is specified.")
} else {
require(shape != null, "shape is required when out is not specified")
outCopy = NDArray.empty(shape, ctx)
}
NDArray.genericNDArrayFunctionInvoke("_sample_uniform", Seq(low, high),
Map("shape" -> outCopy.shape, "out" -> outCopy))
}
/**
* Generate normal(Gaussian) distribution N(mean, stdvar^^2) with shape.
*
* @param loc The mean of the normal distribution.
* @param scale The standard deviation of normal distribution.
* @param shape Output shape of the NDArray generated.
* @param ctx Context of output NDArray, will use default context if not specified.
* @param out Output place holder
* @return The result NDArray with generated result.
*/
def normal(loc: Float,
scale: Float,
shape: Shape = null,
ctx: Context = null,
out: NDArray = null): NDArray = {
var outCopy = out
if (outCopy != null) {
require(shape == null & ctx == null, "shape and ctx is not needed when out is specified.")
} else {
require(shape != null, "shape is required when out is not specified")
outCopy = NDArray.empty(shape, ctx)
}
NDArray.genericNDArrayFunctionInvoke("_sample_normal", Seq.empty[NDArray],
Map("loc" -> loc, "scale" -> scale, "shape" -> outCopy.shape, "out" -> outCopy))
}
/**
* Seed the random number generators in mxnet.
*
* This seed will affect behavior of functions in this module,
* as well as results from executors that contains Random number
* such as Dropout operators.
*
* @param seedState The random number seed to set to all devices.
* @note The random number generator of mxnet is by default device specific.
* This means if you set the same seed, the random number sequence
* generated from GPU0 can be different from CPU.
*/
def seed(seedState: Int): Unit = {
checkCall(_LIB.mxRandomSeed(seedState))
}
}