org.nd4j.linalg.util.FeatureUtil Maven / Gradle / Ivy
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* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://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.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.nd4j.linalg.util;
import lombok.val;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
/**
* Feature matrix related jcuda.utils
*/
public class FeatureUtil {
/**
* Creates an out come vector from the specified inputs
*
* @param index the index of the label
* @param numOutcomes the number of possible outcomes
* @return a binary label matrix used for supervised learning
*/
public static INDArray toOutcomeVector(long index, long numOutcomes) {
if (index > Integer.MAX_VALUE || numOutcomes > Integer.MAX_VALUE)
throw new UnsupportedOperationException();
val nums = new int[(int) numOutcomes];
nums[(int) index] = 1;
return NDArrayUtil.toNDArray(nums);
}
/**
* Creates an out come vector from the specified inputs
*
* @param index the index of the label
* @param numOutcomes the number of possible outcomes
* @return a binary label matrix used for supervised learning
*/
public static INDArray toOutcomeMatrix(int[] index, long numOutcomes) {
INDArray ret = Nd4j.create(index.length, numOutcomes);
for (int i = 0; i < ret.rows(); i++) {
int[] nums = new int[(int) numOutcomes];
nums[index[i]] = 1;
ret.putRow(i, NDArrayUtil.toNDArray(nums));
}
return ret;
}
public static void normalizeMatrix(INDArray toNormalize) {
INDArray columnMeans = toNormalize.mean(0);
toNormalize.subiRowVector(columnMeans);
INDArray std = toNormalize.std(0);
std.addi(Nd4j.scalar(1e-12));
toNormalize.diviRowVector(std);
}
/**
* Divides each row by its max
*
* @param toScale the matrix to divide by its row maxes
*/
public static void scaleByMax(INDArray toScale) {
INDArray scale = toScale.max(1);
for (int i = 0; i < toScale.rows(); i++) {
double scaleBy = scale.getDouble(i);
toScale.putRow(i, toScale.getRow(i).divi(scaleBy));
}
}
/**
* Scales the ndarray columns
* to the given min/max values
*
* @param min the minimum number
* @param max the max number
*/
public static void scaleMinMax(double min, double max, INDArray toScale) {
//X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0)) X_scaled = X_std * (max - min) + min
INDArray min2 = toScale.min(0);
INDArray max2 = toScale.max(0);
INDArray std = toScale.subRowVector(min2).diviRowVector(max2.sub(min2));
INDArray scaled = std.mul(max - min).addi(min);
toScale.assign(scaled);
}
}