/*
* Encog(tm) Core v3.3 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2014 Heaton Research, Inc.
*
* Licensed 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.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package org.encog.ml.data.versatile;
import java.util.List;
import org.encog.EncogError;
import org.encog.mathutil.randomize.generate.GenerateRandom;
import org.encog.ml.data.versatile.columns.ColumnDefinition;
import org.encog.ml.data.versatile.columns.ColumnType;
import org.encog.ml.data.versatile.division.DataDivision;
import org.encog.ml.data.versatile.division.PerformDataDivision;
import org.encog.ml.data.versatile.normalizers.strategies.NormalizationStrategy;
import org.encog.ml.data.versatile.sources.VersatileDataSource;
/**
* The versatile dataset supports several advanced features. 1. it can directly
* read and normalize from a CSV file. 2. It supports virtual time-boxing for
* time series data (the data is NOT expanded in memory). 3. It can easily be
* segmented into smaller datasets.
*/
public class VersatileMLDataSet extends MatrixMLDataSet {
/**
* The source that data is being pulled from.
*/
private VersatileDataSource source;
/**
* The normalization helper.
*/
private NormalizationHelper helper = new NormalizationHelper();
/**
* The number of rows that were analyzed.
*/
private int analyzedRows;
/**
* Construct the data source.
* @param theSource The data source.
*/
public VersatileMLDataSet(VersatileDataSource theSource) {
this.source = theSource;
}
/**
* Find the index of a column.
* @param colDef The column.
* @return The column index.
*/
private int findIndex(ColumnDefinition colDef) {
if (colDef.getIndex() != -1) {
return colDef.getIndex();
}
int index = this.source.columnIndex(colDef.getName());
colDef.setIndex(index);
if (index == -1) {
throw new EncogError("Can't find column");
}
return index;
}
/**
* Analyze the input and determine max, min, mean, etc.
*/
public void analyze() {
String[] line;
// Collect initial stats: sums (for means), highs, lows.
this.source.rewind();
int c = 0;
while ((line = this.source.readLine()) != null) {
c++;
for (int i = 0; i < this.helper.getSourceColumns().size(); i++) {
ColumnDefinition colDef = this.helper.getSourceColumns().get(i);
int index = findIndex(colDef);
String value = line[index];
colDef.analyze(value);
}
}
this.analyzedRows = c;
// Calculate the means, and reset for sd calc.
for (ColumnDefinition colDef : this.helper.getSourceColumns()) {
// Only calculate mean/sd for continuous columns.
if (colDef.getDataType() == ColumnType.continuous) {
colDef.setMean(colDef.getMean() / colDef.getCount());
colDef.setSd(0);
}
}
// Sum the standard deviation
this.source.rewind();
while ((line = this.source.readLine()) != null) {
for (int i = 0; i < this.helper.getSourceColumns().size(); i++) {
ColumnDefinition colDef = this.helper.getSourceColumns().get(i);
String value = line[i];
if (colDef.getDataType() == ColumnType.continuous) {
double d = this.helper.parseDouble(value);
d = colDef.getMean() - d;
d = d * d;
colDef.setSd(colDef.getSd() + d);
}
}
}
// Calculate the standard deviations.
for (ColumnDefinition colDef : this.helper.getSourceColumns()) {
// Only calculate sd for continuous columns.
if (colDef.getDataType() == ColumnType.continuous) {
colDef.setSd(Math.sqrt(colDef.getSd() / colDef.getCount()));
}
}
}
/**
* Normalize the data set, and allocate memory to hold it.
*/
public void normalize() {
NormalizationStrategy strat = this.helper.getNormStrategy();
if (strat == null) {
throw new EncogError(
"Please choose a model type first, with selectMethod.");
}
int normalizedInputColumns = this.helper
.calculateNormalizedInputCount();
int normalizedOutputColumns = this.helper
.calculateNormalizedOutputCount();
int normalizedColumns = normalizedInputColumns
+ normalizedOutputColumns;
setCalculatedIdealSize(normalizedOutputColumns);
setCalculatedInputSize(normalizedInputColumns);
setData(new double[this.analyzedRows][normalizedColumns]);
this.source.rewind();
String[] line;
int row = 0;
while ((line = this.source.readLine()) != null) {
int column = 0;
for (ColumnDefinition colDef : this.helper.getInputColumns()) {
int index = findIndex(colDef);
String value = line[index];
column = this.helper.normalizeToVector(colDef, column,
getData()[row], true, value);
}
for (ColumnDefinition colDef : this.helper.getOutputColumns()) {
int index = findIndex(colDef);
String value = line[index];
column = this.helper.normalizeToVector(colDef, column,
getData()[row], false, value);
}
row++;
}
}
/**
* Define a source column. Used when the file does not contain headings.
* @param name The name of the column.
* @param index The index of the column.
* @param colType The column type.
* @return The column definition.
*/
public ColumnDefinition defineSourceColumn(String name, int index,
ColumnType colType) {
return this.helper.defineSourceColumn(name, index, colType);
}
/**
* @return the helper
*/
public NormalizationHelper getNormHelper() {
return helper;
}
/**
* @param helper
* the helper to set
*/
public void setNormHelper(NormalizationHelper helper) {
this.helper = helper;
}
/**
* Divide, and optionally shuffle, the dataset.
* @param dataDivisionList The desired divisions.
* @param shuffle True, if we should shuffle.
* @param rnd Random number generator, often with a specific seed.
*/
public void divide(List<DataDivision> dataDivisionList, boolean shuffle,
GenerateRandom rnd) {
if (getData() == null) {
throw new EncogError(
"Can't divide, data has not yet been generated/normalized.");
}
PerformDataDivision divide = new PerformDataDivision(shuffle, rnd);
divide.perform(dataDivisionList, this, getCalculatedInputSize(),
getCalculatedIdealSize());
}
/**
* Define an output column.
* @param col The output column.
*/
public void defineOutput(ColumnDefinition col) {
this.helper.getOutputColumns().add(col);
}
/**
* Define an input column.
* @param col The input column.
*/
public void defineInput(ColumnDefinition col) {
this.helper.getInputColumns().add(col);
}
/**
* Define a single column as an output column, all others as inputs.
* @param outputColumn The output column.
*/
public void defineSingleOutputOthersInput(ColumnDefinition outputColumn) {
this.helper.clearInputOutput();
for (ColumnDefinition colDef : this.helper.getSourceColumns()) {
if (colDef == outputColumn) {
defineOutput(colDef);
} else if (colDef.getDataType() != ColumnType.ignore) {
defineInput(colDef);
}
}
}
/**
* Define a source column.
* @param name The name of the source column.
* @param colType The column type.
* @return The column definition.
*/
public ColumnDefinition defineSourceColumn(String name, ColumnType colType) {
return this.helper.defineSourceColumn(name, -1, colType);
}
}