xuyang1706 commented on a change in pull request #8631: [FLINK-12745][ml] add sparse and dense vector class, and dense matrix class with basic operations. URL: https://github.com/apache/flink/pull/8631#discussion_r314708905
########## File path: flink-ml-parent/flink-ml-lib/src/main/java/org/apache/flink/ml/common/linalg/DenseVector.java ########## @@ -0,0 +1,463 @@ +/* + * 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.flink.ml.common.linalg; + +import org.apache.commons.lang3.StringUtils; + +import java.util.Arrays; +import java.util.Random; + +/** + * A dense vector represented by a values array. + */ +public class DenseVector extends Vector { + /** + * The array holding the vector data. + * <p> + * Package private to allow access from {@link MatVecOp} and {@link BLAS}. + */ + double[] data; + + /** + * Create a zero size vector. + */ + public DenseVector() { + this(0); + } + + /** + * Create a size n vector with all elements zero. + * + * @param n Size of the vector. + */ + public DenseVector(int n) { + this.data = new double[n]; + } + + /** + * Create a dense vector with the user provided data. + * + * @param data The vector data. + */ + public DenseVector(double[] data) { + this.data = data; + } + + /** + * Get the data array. + */ + public double[] getData() { + return this.data; + } + + /** + * Set the data array. + */ + public void setData(double[] data) { + this.data = data; + } + + /** + * Create a dense vector with all elements one. + * + * @param n Size of the vector. + * @return The newly created dense vector. + */ + public static DenseVector ones(int n) { + DenseVector r = new DenseVector(n); + Arrays.fill(r.data, 1.0); + return r; + } + + /** + * Create a dense vector with all elements zero. + * + * @param n Size of the vector. + * @return The newly created dense vector. + */ + public static DenseVector zeros(int n) { + DenseVector r = new DenseVector(n); + Arrays.fill(r.data, 0.0); + return r; + } + + /** + * Create a dense vector with random values uniformly distributed in the range of [0.0, 1.0]. + * + * @param n Size of the vector. + * @return The newly created dense vector. + */ + public static DenseVector rand(int n) { + Random random = new Random(); + DenseVector v = new DenseVector(n); + for (int i = 0; i < n; i++) { + v.data[i] = random.nextDouble(); + } + return v; + } + + /** + * Delimiter between elements. + */ + private static final char ELEMENT_DELIMITER = ' '; + + @Override + public String serialize() { + StringBuilder sbd = new StringBuilder(); + + for (int i = 0; i < data.length; i++) { + sbd.append(data[i]); + if (i < data.length - 1) { + sbd.append(ELEMENT_DELIMITER); + } + } + return sbd.toString(); + } + + /** + * Parse the dense vector from a formatted string. + * + * <p>The format of a dense vector is comma separated values such as "1 2 3 4". + * + * @param str A string of space separated values. + * @return The parsed vector. + */ + public static DenseVector deserialize(String str) { + if (org.apache.flink.util.StringUtils.isNullOrWhitespaceOnly(str)) { + return new DenseVector(); + } + + int len = str.length(); + + int inDataBuffPos = 0; + boolean isInBuff = false; + + for (int i = 0; i < len; ++i) { + if (str.charAt(i) == ELEMENT_DELIMITER) { + if (isInBuff) { + inDataBuffPos++; + } + + isInBuff = false; + } else { + isInBuff = true; + } + } + + if (isInBuff) { + inDataBuffPos++; + } + + double[] data = new double[inDataBuffPos]; + int lastestInCharBuffPos = 0; + + inDataBuffPos = 0; + isInBuff = false; + + for (int i = 0; i < len; ++i) { + if (str.charAt(i) == ELEMENT_DELIMITER) { + if (isInBuff) { + data[inDataBuffPos++] = Double.parseDouble( + StringUtils.substring(str, lastestInCharBuffPos, i).trim() + ); + + lastestInCharBuffPos = i + 1; + } + + isInBuff = false; + } else { + isInBuff = true; + } + } + + if (isInBuff) { + data[inDataBuffPos] = Double.valueOf( + StringUtils.substring(str, lastestInCharBuffPos).trim() + ); + } + + return new DenseVector(data); + } + + @Override + public DenseVector clone() { + return new DenseVector(this.data.clone()); + } + + @Override + public String toString() { + return this.serialize(); + } + + @Override + public int size() { + return data.length; + } + + @Override + public double get(int i) { + return data[i]; + } + + @Override + public void set(int i, double d) { + data[i] = d; + } + + @Override + public void add(int i, double d) { + data[i] += d; + } + + @Override + public double normL1() { + double d = 0; + for (double t : data) { + d += Math.abs(t); + } + return d; + } + + @Override + public double normL2() { + double d = 0; + for (double t : data) { + d += t * t; + } + return Math.sqrt(d); + } + + @Override + public double normL2Square() { + double d = 0; + for (double t : data) { + d += t * t; + } + return d; + } + + @Override + public double normInf() { + double d = 0; + for (double t : data) { + d = Math.max(Math.abs(t), d); + } + return d; + } + + @Override + public DenseVector slice(int[] indices) { + double[] values = new double[indices.length]; + for (int i = 0; i < indices.length; ++i) { + if (indices[i] >= data.length) { + throw new RuntimeException("Index is larger than vector size."); + } + values[i] = data[indices[i]]; + } + return new DenseVector(values); + } + + @Override + public DenseVector prefix(double d) { + double[] data = new double[this.size() + 1]; + data[0] = d; + System.arraycopy(this.data, 0, data, 1, this.data.length); + return new DenseVector(data); + } + + @Override + public DenseVector append(double d) { + double[] data = new double[this.size() + 1]; + System.arraycopy(this.data, 0, data, 0, this.data.length); + data[this.size()] = d; + return new DenseVector(data); + } + + @Override + public void scaleEqual(double d) { + BLAS.scal(d, this); + } + + @Override + public DenseVector plus(Vector other) { + DenseVector r = this.clone(); + if (other instanceof DenseVector) { + BLAS.axpy(1.0, (DenseVector) other, r); + } else { + BLAS.axpy(1.0, (SparseVector) other, r); + } + return r; + } + + @Override + public DenseVector minus(Vector other) { + DenseVector r = this.clone(); + if (other instanceof DenseVector) { + BLAS.axpy(-1.0, (DenseVector) other, r); + } else { + BLAS.axpy(-1.0, (SparseVector) other, r); + } + return r; + } + + @Override + public DenseVector scale(double d) { + DenseVector r = this.clone(); + BLAS.scal(d, r); + return r; + } + + @Override + public double dot(Vector vec) { + if (vec instanceof DenseVector) { + return BLAS.dot(this, (DenseVector) vec); + } else { + return ((SparseVector) vec).dot(this); Review comment: thx. 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