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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 org.apache.commons.math.stat.correlation;
+
+import org.apache.commons.math.MathRuntimeException;
+import org.apache.commons.math.exception.util.LocalizedFormats;
+import org.apache.commons.math.linear.BlockRealMatrix;
+import org.apache.commons.math.linear.RealMatrix;
+import org.apache.commons.math.stat.ranking.NaturalRanking;
+import org.apache.commons.math.stat.ranking.RankingAlgorithm;
+
+/**
+ * <p>Spearman's rank correlation. This implementation performs a rank
+ * transformation on the input data and then computes {@link PearsonsCorrelation}
+ * on the ranked data.</p>
+ *
+ * <p>By default, ranks are computed using {@link NaturalRanking} with default
+ * strategies for handling NaNs and ties in the data (NaNs maximal, ties averaged).
+ * The ranking algorithm can be set using a constructor argument.</p>
+ *
+ * @since 2.0
+ * @version $Revision: 983921 $ $Date: 2010-08-10 12:46:06 +0200 (mar. 10 août 2010) $
+ */
+
+public class SpearmansCorrelation {
+
+    /** Input data */
+    private final RealMatrix data;
+
+    /** Ranking algorithm  */
+    private final RankingAlgorithm rankingAlgorithm;
+
+    /** Rank correlation */
+    private final PearsonsCorrelation rankCorrelation;
+
+    /**
+     * Create a SpearmansCorrelation with the given input data matrix
+     * and ranking algorithm.
+     *
+     * @param dataMatrix matrix of data with columns representing
+     * variables to correlate
+     * @param rankingAlgorithm ranking algorithm
+     */
+    public SpearmansCorrelation(final RealMatrix dataMatrix, final RankingAlgorithm rankingAlgorithm) {
+        this.data = dataMatrix.copy();
+        this.rankingAlgorithm = rankingAlgorithm;
+        rankTransform(data);
+        rankCorrelation = new PearsonsCorrelation(data);
+    }
+
+    /**
+     * Create a SpearmansCorrelation from the given data matrix.
+     *
+     * @param dataMatrix matrix of data with columns representing
+     * variables to correlate
+     */
+    public SpearmansCorrelation(final RealMatrix dataMatrix) {
+        this(dataMatrix, new NaturalRanking());
+    }
+
+    /**
+     * Create a SpearmansCorrelation without data.
+     */
+    public SpearmansCorrelation() {
+        data = null;
+        this.rankingAlgorithm = new NaturalRanking();
+        rankCorrelation = null;
+    }
+
+    /**
+     * Calculate the Spearman Rank Correlation Matrix.
+     *
+     * @return Spearman Rank Correlation Matrix
+     */
+    public RealMatrix getCorrelationMatrix() {
+        return rankCorrelation.getCorrelationMatrix();
+    }
+
+    /**
+     * Returns a {@link PearsonsCorrelation} instance constructed from the
+     * ranked input data. That is,
+     * <code>new SpearmansCorrelation(matrix).getRankCorrelation()</code>
+     * is equivalent to
+     * <code>new PearsonsCorrelation(rankTransform(matrix))</code> where
+     * <code>rankTransform(matrix)</code> is the result of applying the
+     * configured <code>RankingAlgorithm</code> to each of the columns of
+     * <code>matrix.</code>
+     *
+     * @return PearsonsCorrelation among ranked column data
+     */
+    public PearsonsCorrelation getRankCorrelation() {
+        return rankCorrelation;
+    }
+
+    /**
+     * Computes the Spearman's rank correlation matrix for the columns of the
+     * input matrix.
+     *
+     * @param matrix matrix with columns representing variables to correlate
+     * @return correlation matrix
+     */
+    public RealMatrix computeCorrelationMatrix(RealMatrix matrix) {
+        RealMatrix matrixCopy = matrix.copy();
+        rankTransform(matrixCopy);
+        return new PearsonsCorrelation().computeCorrelationMatrix(matrixCopy);
+    }
+
+    /**
+     * Computes the Spearman's rank correlation matrix for the columns of the
+     * input rectangular array.  The columns of the array represent values
+     * of variables to be correlated.
+     *
+     * @param matrix matrix with columns representing variables to correlate
+     * @return correlation matrix
+     */
+    public RealMatrix computeCorrelationMatrix(double[][] matrix) {
+       return computeCorrelationMatrix(new BlockRealMatrix(matrix));
+    }
+
+    /**
+     * Computes the Spearman's rank correlation coefficient between the two arrays.
+     *
+     * </p>Throws IllegalArgumentException if the arrays do not have the same length
+     * or their common length is less than 2</p>
+     *
+     * @param xArray first data array
+     * @param yArray second data array
+     * @return Returns Spearman's rank correlation coefficient for the two arrays
+     * @throws  IllegalArgumentException if the arrays lengths do not match or
+     * there is insufficient data
+     */
+    public double correlation(final double[] xArray, final double[] yArray)
+    throws IllegalArgumentException {
+        if (xArray.length != yArray.length) {
+            throw MathRuntimeException.createIllegalArgumentException(
+                  LocalizedFormats.DIMENSIONS_MISMATCH_SIMPLE, xArray.length, yArray.length);
+        } else if (xArray.length < 2) {
+            throw MathRuntimeException.createIllegalArgumentException(
+                  LocalizedFormats.INSUFFICIENT_DIMENSION, xArray.length, 2);
+        } else {
+            return new PearsonsCorrelation().correlation(rankingAlgorithm.rank(xArray),
+                    rankingAlgorithm.rank(yArray));
+        }
+    }
+
+    /**
+     * Applies rank transform to each of the columns of <code>matrix</code>
+     * using the current <code>rankingAlgorithm</code>
+     *
+     * @param matrix matrix to transform
+     */
+    private void rankTransform(RealMatrix matrix) {
+        for (int i = 0; i < matrix.getColumnDimension(); i++) {
+            matrix.setColumn(i, rankingAlgorithm.rank(matrix.getColumn(i)));
+        }
+    }
+}