yzhliu commented on a change in pull request #13162: [MXNET-1198] MXNet Java API URL: https://github.com/apache/incubator-mxnet/pull/13162#discussion_r233611420
########## File path: scala-package/core/src/main/scala/org/apache/mxnet/javaapi/NDArray.scala ########## @@ -0,0 +1,387 @@ +/* + * 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 org.apache.mxnet.javaapi + +import org.apache.mxnet.javaapi.DType.DType + +import collection.JavaConverters._ + +@AddJNDArrayAPIs(false) +object NDArray extends NDArrayBase { + implicit def fromNDArray(nd: org.apache.mxnet.NDArray): NDArray = new NDArray(nd) + + implicit def toNDArray(jnd: NDArray): org.apache.mxnet.NDArray = jnd.nd + + def waitall(): Unit = org.apache.mxnet.NDArray.waitall() + + /** + * One hot encoding indices into matrix out. + * @param indices An NDArray containing indices of the categorical features. + * @param out The result holder of the encoding. + * @return Same as out. + */ + def onehotEncode(indices: NDArray, out: NDArray): NDArray + = org.apache.mxnet.NDArray.onehotEncode(indices, out) + + /** + * Create an empty uninitialized new NDArray, with specified shape. + * + * @param shape shape of the NDArray. + * @param ctx The context of the NDArray. + * + * @return The created NDArray. + */ + def empty(shape: Shape, ctx: Context, dtype: DType.DType): NDArray + = org.apache.mxnet.NDArray.empty(shape, ctx, dtype) + def empty(ctx: Context, shape: Array[Int]): NDArray + = org.apache.mxnet.NDArray.empty(new Shape(shape), ctx) + def empty(ctx : Context, shape : java.util.List[java.lang.Integer]) : NDArray + = org.apache.mxnet.NDArray.empty(new Shape(shape), ctx) + + /** + * Create a new NDArray filled with 0, with specified shape. + * + * @param shape shape of the NDArray. + * @param ctx The context of the NDArray. + * + * @return The created NDArray. + */ + def zeros(shape: Shape, ctx: Context, dtype: DType.DType): NDArray + = org.apache.mxnet.NDArray.zeros(shape, ctx, dtype) + def zeros(ctx: Context, shape: Array[Int]): NDArray + = org.apache.mxnet.NDArray.zeros(new Shape(shape), ctx) + def zeros(ctx : Context, shape : java.util.List[java.lang.Integer]) : NDArray + = org.apache.mxnet.NDArray.zeros(new Shape(shape), ctx) + + /** + * Create a new NDArray filled with 1, with specified shape. + * @param shape shape of the NDArray. + * @param ctx The context of the NDArray. + * @return The created NDArray. + */ + def ones(shape: Shape, ctx: Context, dtype: DType.DType): NDArray + = org.apache.mxnet.NDArray.ones(shape, ctx, dtype) + def ones(ctx: Context, shape: Array[Int]): NDArray + = org.apache.mxnet.NDArray.ones(new Shape(shape), ctx) + def ones(ctx : Context, shape : java.util.List[java.lang.Integer]) : NDArray + = org.apache.mxnet.NDArray.ones(new Shape(shape), ctx) + + /** + * Create a new NDArray filled with given value, with specified shape. + * @param shape shape of the NDArray. + * @param value value to be filled with + * @param ctx The context of the NDArray + */ + def full(shape: Shape, value: Float, ctx: Context): NDArray + = org.apache.mxnet.NDArray.full(shape, value, ctx) + + def power(lhs: NDArray, rhs: NDArray): NDArray = org.apache.mxnet.NDArray.power(lhs, rhs) + def power(lhs: NDArray, rhs: Float): NDArray = org.apache.mxnet.NDArray.power(lhs, rhs) + def power(lhs: Float, rhs: NDArray): NDArray = org.apache.mxnet.NDArray.power(lhs, rhs) + + def maximum(lhs: NDArray, rhs: NDArray): NDArray = org.apache.mxnet.NDArray.maximum(lhs, rhs) + def maximum(lhs: NDArray, rhs: Float): NDArray = org.apache.mxnet.NDArray.maximum(lhs, rhs) + def maximum(lhs: Float, rhs: NDArray): NDArray = org.apache.mxnet.NDArray.maximum(lhs, rhs) + + def minimum(lhs: NDArray, rhs: NDArray): NDArray = org.apache.mxnet.NDArray.minimum(lhs, rhs) + def minimum(lhs: NDArray, rhs: Float): NDArray = org.apache.mxnet.NDArray.minimum(lhs, rhs) + def minimum(lhs: Float, rhs: NDArray): NDArray = org.apache.mxnet.NDArray.minimum(lhs, rhs) + + + /** + * Returns the result of element-wise **equal to** (==) comparison operation with broadcasting. + * For each element in input arrays, return 1(true) if corresponding elements are same, + * otherwise return 0(false). + */ + def equal(lhs: NDArray, rhs: NDArray): NDArray = org.apache.mxnet.NDArray.equal(lhs, rhs) + def equal(lhs: NDArray, rhs: Float): NDArray = org.apache.mxnet.NDArray.equal(lhs, rhs) + + /** + * Returns the result of element-wise **not equal to** (!=) comparison operation + * with broadcasting. + * For each element in input arrays, return 1(true) if corresponding elements are different, + * otherwise return 0(false). + */ + def notEqual(lhs: NDArray, rhs: NDArray): NDArray = org.apache.mxnet.NDArray.notEqual(lhs, rhs) + def notEqual(lhs: NDArray, rhs: Float): NDArray = org.apache.mxnet.NDArray.notEqual(lhs, rhs) + + /** + * Returns the result of element-wise **greater than** (>) comparison operation + * with broadcasting. + * For each element in input arrays, return 1(true) if lhs elements are greater than rhs, + * otherwise return 0(false). + */ + def greater(lhs: NDArray, rhs: NDArray): NDArray = org.apache.mxnet.NDArray.greater(lhs, rhs) + def greater(lhs: NDArray, rhs: Float): NDArray = org.apache.mxnet.NDArray.greater(lhs, rhs) + + /** + * Returns the result of element-wise **greater than or equal to** (>=) comparison + * operation with broadcasting. + * For each element in input arrays, return 1(true) if lhs elements are greater than equal to rhs + * otherwise return 0(false). + */ + def greaterEqual(lhs: NDArray, rhs: NDArray): NDArray + = org.apache.mxnet.NDArray.greaterEqual(lhs, rhs) + def greaterEqual(lhs: NDArray, rhs: Float): NDArray + = org.apache.mxnet.NDArray.greaterEqual(lhs, rhs) + + /** + * Returns the result of element-wise **lesser than** (<) comparison operation + * with broadcasting. + * For each element in input arrays, return 1(true) if lhs elements are less than rhs, + * otherwise return 0(false). + */ + def lesser(lhs: NDArray, rhs: NDArray): NDArray = org.apache.mxnet.NDArray.lesser(lhs, rhs) + def lesser(lhs: NDArray, rhs: Float): NDArray = org.apache.mxnet.NDArray.lesser(lhs, rhs) + + /** + * Returns the result of element-wise **lesser than or equal to** (<=) comparison + * operation with broadcasting. + * For each element in input arrays, return 1(true) if lhs elements are + * lesser than equal to rhs, otherwise return 0(false). + */ + def lesserEqual(lhs: NDArray, rhs: NDArray): NDArray + = org.apache.mxnet.NDArray.lesserEqual(lhs, rhs) + def lesserEqual(lhs: NDArray, rhs: Float): NDArray + = org.apache.mxnet.NDArray.lesserEqual(lhs, rhs) + + /** + * Create a new NDArray that copies content from source_array. + * @param sourceArr Source data to create NDArray from. + * @param shape shape of the NDArray + * @param ctx The context of the NDArray, default to current default context. + * @return The created NDArray. + */ + def array(sourceArr: java.util.List[java.lang.Float], shape: Shape, ctx: Context = null): NDArray + = org.apache.mxnet.NDArray.array( + sourceArr.asScala.map(ele => Float.unbox(ele)).toArray, shape, ctx) + + /** + * Returns evenly spaced values within a given interval. + * Values are generated within the half-open interval [`start`, `stop`). In other + * words, the interval includes `start` but excludes `stop`. + * @param start Start of interval. + * @param stop End of interval. + * @param step Spacing between values. + * @param repeat Number of times to repeat each element. + * @param ctx Device context. + * @param dType The data type of the `NDArray`. + * @return NDArray of evenly spaced values in the specified range. + */ + def arange(start: Float, stop: Float, step: Float, repeat: Int, + ctx: Context, dType: DType.DType): NDArray = + org.apache.mxnet.NDArray.arange(start, Some(stop), step, repeat, ctx, dType) +} + +/** + * NDArray object in mxnet. + * NDArray is basic ndarray/Tensor like data structure in mxnet. <br /> + * <b> + * NOTE: NDArray is stored in native memory. Use NDArray in a try-with-resources() construct + * or a [[ResourceScope]] in a try-with-resource to have them automatically disposed. You can + * explicitly control the lifetime of NDArray by calling dispose manually. Failure to do this + * will result in leaking native memory. + * </b> + */ +class NDArray(val nd : org.apache.mxnet.NDArray ) { + + def this(arr : Array[Float], shape : Shape, ctx : Context) = { + this(org.apache.mxnet.NDArray.array(arr, shape, ctx)) + } + + def this(arr : java.util.List[java.lang.Float], shape : Shape, ctx : Context) = { + this(NDArray.array(arr, shape, ctx)) + } + + def serialize() : Array[Byte] = nd.serialize() + + /** + * Release the native memory. <br /> + * The NDArrays it depends on will NOT be disposed. <br /> + * The object shall never be used after it is disposed. + */ + def dispose() : Unit = nd.dispose() + + /** + * Dispose all NDArrays who help to construct this array. <br /> + * e.g. (a * b + c).disposeDeps() will dispose a, b, c (including their deps) and a * b + * @return this array + */ + def disposeDeps() : NDArray = nd.disposeDepsExcept() + // def disposeDepsExcept(arr : Array[NDArray]) : NDArray = nd.disposeDepsExcept() Review comment: remove or uncomment ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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