List of usage examples for org.apache.mahout.math Matrix viewColumn
Vector viewColumn(int column);
From source file:ca.uwaterloo.cpami.mahout.matrix.utils.GramSchmidt.java
License:Apache License
public static void main(String[] args) throws IOException { //final Configuration conf = new Configuration(); //final FileSystem fs = FileSystem.get(conf); //final SequenceFile.Reader reader = new SequenceFile.Reader(fs, // new Path("R1.dat"), conf); //IntWritable key = new IntWritable(); //VectorWritable vec = new VectorWritable(); Matrix mat = new SparseMatrix(1500, 100); //SparseRealMatrix mat2 = new OpenMapRealMatrix(12419,1500 ); BufferedReader reader = new BufferedReader(new FileReader("R.3.csv")); String line = null;//from ww w. j a v a 2 s .co m while ((line = reader.readLine()) != null) { String[] parts = line.split(","); mat.set(Integer.parseInt(parts[0]), Integer.parseInt(parts[1]), Double.parseDouble(parts[2])); /* Vector v = vec.get(); int i=0; Iterator<Vector.Element> itr = v.iterateNonZero(); while(itr.hasNext()){ double elem = itr.next().get(); if(elem !=0) mat2.setEntry(i, key.get(), elem); i++; } */ } //mat = mat.transpose(); System.out.println(mat.viewColumn(0).isDense()); System.out.println(mat.viewRow(0).isDense()); mat = mat.transpose(); GramSchmidt.orthonormalizeColumns(mat); /* System.out.println("started QR"); System.out.println(Runtime.getRuntime().maxMemory()); System.out.println(Runtime.getRuntime().maxMemory()-Runtime.getRuntime().freeMemory()); QRDecomposition qr = new QRDecomposition(mat2); System.out.println(qr.getQ().getColumnDimension()); System.out.println(qr.getQ().getRowDimension()); */ //mat = mat.transpose(); //storeSparseColumns(mat); //for (int i = 0; i < 10; i++) { // System.out.println(mat.viewRow(i).getNumNondefaultElements()); //} }
From source file:ca.uwaterloo.cpami.mahout.matrix.utils.GramSchmidt.java
License:Apache License
public static void storeSparseColumns(Matrix mat) { int numCols = mat.numCols(); int numRows = mat.numRows(); for (int i = 0; i < numCols; i++) { Vector sparseVect = new RandomAccessSparseVector(numRows); Vector col = mat.viewColumn(i); Iterator<Vector.Element> itr = col.iterateNonZero(); while (itr.hasNext()) { Element elem = itr.next(); if (elem.get() != 0) { System.out.println(elem.get()); sparseVect.set(elem.index(), elem.get()); }/* www .j av a 2s . c om*/ } System.out.println(sparseVect.getNumNondefaultElements()); mat.assignColumn(i, sparseVect); System.out.println(mat.viewColumn(i).getNumNondefaultElements()); System.exit(1); } }
From source file:com.mapr.stats.bandit.ContextualBayesBanditTest.java
License:Apache License
@Test public void testConvergence() { final Random rand = RandomUtils.getRandom(); Matrix recipes = new DenseMatrix(100, 10).assign(new DoubleFunction() { @Override/*w ww . j a v a2 s . c om*/ public double apply(double arg1) { return rand.nextDouble() < 0.2 ? 1 : 0; } }); recipes.viewColumn(9).assign(1); Vector actualWeights = new DenseVector(new double[] { 1, 0.25, -0.25, 0, 0, 0, 0, 0, 0, -1 }); Vector probs = recipes.times(actualWeights); ContextualBayesBandit banditry = new ContextualBayesBandit(recipes); for (int i = 0; i < 1000; i++) { int k = banditry.sample(); final boolean success = rand.nextDouble() < probs.get(k); banditry.train(k, success); } }
From source file:com.twitter.algebra.AlgebraCommon.java
License:Apache License
/** * Multiply a vector with a matrix//from ww w .j ava2 s. c om * @param vector V * @param matrix M * @param resVector will be filled with V * M * @return V * M */ public static Vector vectorTimesMatrix(Vector vector, Matrix matrix, DenseVector resVector) { int nCols = matrix.numCols(); for (int c = 0; c < nCols; c++) { Double resDouble = vector.dot(matrix.viewColumn(c)); resVector.set(c, resDouble); } return resVector; }
From source file:org.qcri.pca.PCACommon.java
static Vector sparseVectorTimesMatrix(Vector vector, Matrix matrix, DenseVector resVector) { int nCols = matrix.numCols(); for (int c = 0; c < nCols; c++) { Double resDouble = vector.dot(matrix.viewColumn(c)); resVector.set(c, resDouble);/* w ww . java 2s. c om*/ } return resVector; }
From source file:org.qcri.pca.SPCADriver.java
/** * Run PPCA sequentially given the small input Y which fit into memory This * could be used also on sampled data from a distributed matrix * /*from w w w . j a v a2 s . co m*/ * Note: this implementation ignore NaN values by replacing them with 0 * * @param conf * the configuration * @param centralY * the input matrix * @param initVal * the initial values for C and ss * @param MAX_ROUNDS * maximum number of iterations * @return the error * @throws Exception */ double runSequential(Configuration conf, Matrix centralY, InitialValues initVal, final int MAX_ROUNDS) throws Exception { Matrix centralC = initVal.C; double ss = initVal.ss; final int nRows = centralY.numRows(); final int nCols = centralY.numCols(); final int nPCs = centralC.numCols(); final float threshold = 0.00001f; log.info("tracec= " + PCACommon.trace(centralC)); //ignore NaN elements by replacing them with 0 for (int r = 0; r < nRows; r++) for (int c = 0; c < nCols; c++) if (new Double(centralY.getQuick(r, c)).isNaN()) { centralY.setQuick(r, c, 0); } //centralize and normalize the input matrix Vector mean = centralY.aggregateColumns(new VectorFunction() { @Override public double apply(Vector v) { return v.zSum() / nRows; } }); //also normalize the matrix by dividing each element by its columns range Vector spanVector = new DenseVector(nCols); for (int c = 0; c < nCols; c++) { Vector col = centralY.viewColumn(c); double max = col.maxValue(); double min = col.minValue(); double span = max - min; spanVector.setQuick(c, span); } for (int r = 0; r < nRows; r++) for (int c = 0; c < nCols; c++) centralY.set(r, c, (centralY.get(r, c) - mean.get(c)) / (spanVector.getQuick(c) != 0 ? spanVector.getQuick(c) : 1)); Matrix centralCtC = centralC.transpose().times(centralC); log.info("tracectc= " + PCACommon.trace(centralCtC)); log.info("traceinvctc= " + PCACommon.trace(inv(centralCtC))); log.info("traceye= " + PCACommon.trace(centralY)); log.info("SSSSSSSSSSSSSSSSSSSSSSSSSSSS " + ss); int count = 1; // old = Inf; double old = Double.MAX_VALUE; // -------------------------- EM Iterations // while count Matrix centralX = null; int round = 0; while (round < MAX_ROUNDS && count > 0) { round++; // Sx = inv( eye(d) + CtC/ss ); Matrix Sx = eye(nPCs).times(ss).plus(centralCtC); Sx = inv(Sx); // X = Ye*C*(Sx/ss); centralX = centralY.times(centralC).times(Sx.transpose()); // XtX = X'*X + ss * Sx; Matrix centralXtX = centralX.transpose().times(centralX).plus(Sx.times(ss)); // C = (Ye'*X) / XtX; Matrix tmpInv = inv(centralXtX); centralC = centralY.transpose().times(centralX).times(tmpInv); // CtC = C'*C; centralCtC = centralC.transpose().times(centralC); // ss = ( sum(sum( (X*C'-Ye).^2 )) + trace(XtX*CtC) - 2*xcty ) /(N*D); double norm2 = centralY.clone().assign(new DoubleFunction() { @Override public double apply(double arg1) { return arg1 * arg1; } }).zSum(); ss = norm2 + PCACommon.trace(centralXtX.times(centralCtC)); //ss3 = sum (X(i:0) * C' * Y(i,:)') DenseVector resVector = new DenseVector(nCols); double xctyt = 0; for (int i = 0; i < nRows; i++) { PCACommon.vectorTimesMatrixTranspose(centralX.viewRow(i), centralC, resVector); double res = resVector.dot(centralY.viewRow(i)); xctyt += res; } ss -= 2 * xctyt; ss /= (nRows * nCols); log.info("SSSSSSSSSSSSSSSSSSSSSSSSSSSS " + ss); double traceSx = PCACommon.trace(Sx); double traceX = PCACommon.trace(centralX); double traceSumXtX = PCACommon.trace(centralXtX); double traceC = PCACommon.trace(centralC); double traceCtC = PCACommon.trace(centralCtC); log.info("TTTTTTTTTTTTTTTTT " + traceSx + " " + traceX + " " + traceSumXtX + " " + traceC + " " + traceCtC + " " + 0); double objective = ss; double rel_ch = Math.abs(1 - objective / old); old = objective; count++; if (rel_ch < threshold && count > 5) count = 0; log.info("Objective: %.6f relative change: %.6f \n", objective, rel_ch); } double norm1Y = centralY.aggregateColumns(new VectorNorm1()).maxValue(); log.info("Norm1 of Ye is: " + norm1Y); Matrix newYerror = centralY.minus(centralX.times(centralC.transpose())); double norm1Err = newYerror.aggregateColumns(new VectorNorm1()).maxValue(); log.info("Norm1 of the reconstruction error is: " + norm1Err); initVal.C = centralC; initVal.ss = ss; return norm1Err / norm1Y; }
From source file:org.qcri.pca.SPCADriver.java
/** * Run PPCA sequentially given the small input Y which fit into memory This * could be used also on sampled data from a distributed matrix * /*w w w . j a v a2 s .c o m*/ * Note: this implementation ignore NaN values by replacing them with 0 * * @param conf * the configuration * @param centralY * the input matrix * @param initVal * the initial values for C and ss * @param MAX_ROUNDS * maximum number of iterations * @return the error * @throws Exception */ double runSequential_JacobVersion(Configuration conf, Matrix centralY, InitialValues initVal, final int MAX_ROUNDS) { Matrix centralC = initVal.C;// the current implementation doesn't use initial ss of // initVal final int nRows = centralY.numRows(); final int nCols = centralY.numCols(); final int nPCs = centralC.numCols(); final float threshold = 0.00001f; log.info("tracec= " + PCACommon.trace(centralC)); // Y = Y - mean(Ye) // Also normalize the matrix for (int r = 0; r < nRows; r++) for (int c = 0; c < nCols; c++) if (new Double(centralY.getQuick(r, c)).isNaN()) { centralY.setQuick(r, c, 0); } Vector mean = centralY.aggregateColumns(new VectorFunction() { @Override public double apply(Vector v) { return v.zSum() / nRows; } }); Vector spanVector = new DenseVector(nCols); for (int c = 0; c < nCols; c++) { Vector col = centralY.viewColumn(c); double max = col.maxValue(); double min = col.minValue(); double span = max - min; spanVector.setQuick(c, span); } for (int r = 0; r < nRows; r++) for (int c = 0; c < nCols; c++) centralY.set(r, c, (centralY.get(r, c) - mean.get(c)) / (spanVector.getQuick(c) != 0 ? spanVector.getQuick(c) : 1)); // -------------------------- initialization // CtC = C'*C; Matrix centralCtC = centralC.transpose().times(centralC); log.info("tracectc= " + PCACommon.trace(centralCtC)); log.info("traceinvctc= " + PCACommon.trace(inv(centralCtC))); log.info("traceye= " + PCACommon.trace(centralY)); // X = Ye * C * inv(CtC); Matrix centralX = centralY.times(centralC).times(inv(centralCtC)); log.info("tracex= " + PCACommon.trace(centralX)); // recon = X * C'; Matrix recon = centralX.times(centralC.transpose()); log.info("tracerec= " + PCACommon.trace(recon)); // ss = sum(sum((recon-Ye).^2)) / (N*D-missing); double ss = recon.minus(centralY).assign(new DoubleFunction() { @Override public double apply(double arg1) { return arg1 * arg1; } }).zSum() / (nRows * nCols); log.info("SSSSSSSSSSSSSSSSSSSSSSSSSSSS " + ss); int count = 1; // old = Inf; double old = Double.MAX_VALUE; // -------------------------- EM Iterations // while count int round = 0; while (round < MAX_ROUNDS && count > 0) { round++; // ------------------ E-step, (co)variances // Sx = inv( eye(d) + CtC/ss ); Matrix centralSx = eye(nPCs).plus(centralCtC.divide(ss)); centralSx = inv(centralSx); // ------------------ E-step expected value // X = Ye*C*(Sx/ss); centralX = centralY.times(centralC).times(centralSx.divide(ss)); // ------------------ M-step // SumXtX = X'*X; Matrix centralSumXtX = centralX.transpose().times(centralX); // C = (Ye'*X) / (SumXtX + N*Sx ); Matrix tmpInv = inv(centralSumXtX.plus(centralSx.times(nRows))); centralC = centralY.transpose().times(centralX).times(tmpInv); // CtC = C'*C; centralCtC = centralC.transpose().times(centralC); // ss = ( sum(sum( (X*C'-Ye).^2 )) + N*sum(sum(CtC.*Sx)) + // missing*ss_old ) /(N*D); recon = centralX.times(centralC.transpose()); double error = recon.minus(centralY).assign(new DoubleFunction() { @Override public double apply(double arg1) { return arg1 * arg1; } }).zSum(); ss = error + nRows * dot(centralCtC.clone(), centralSx).zSum(); ss /= (nRows * nCols); log.info("SSSSSSSSSSSSSSSSSSSSSSSSSSSS " + ss); double traceSx = PCACommon.trace(centralSx); double traceX = PCACommon.trace(centralX); double traceSumXtX = PCACommon.trace(centralSumXtX); double traceC = PCACommon.trace(centralC); double traceCtC = PCACommon.trace(centralCtC); log.info("TTTTTTTTTTTTTTTTT " + traceSx + " " + traceX + " " + traceSumXtX + " " + traceC + " " + traceCtC + " " + 0); // objective = N*D + N*(D*log(ss) +PCACommon.trace(Sx)-log(det(Sx)) ) // +PCACommon.trace(SumXtX) -missing*log(ss_old); double objective = nRows * nCols + nRows * (nCols * Math.log(ss) + PCACommon.trace(centralSx) - Math.log(centralSx.determinant())) + PCACommon.trace(centralSumXtX); double rel_ch = Math.abs(1 - objective / old); old = objective; count++; if (rel_ch < threshold && count > 5) count = 0; System.out.printf("Objective: %.6f relative change: %.6f \n", objective, rel_ch); } double norm1Y = centralY.aggregateColumns(new VectorNorm1()).maxValue(); log.info("Norm1 of Y is: " + norm1Y); Matrix newYerror = centralY.minus(centralX.times(centralC.transpose())); double norm1Err = newYerror.aggregateColumns(new VectorNorm1()).maxValue(); log.info("Norm1 of the reconstruction error is: " + norm1Err); initVal.C = centralC; initVal.ss = ss; return norm1Err / norm1Y; }