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The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This version represents the developer version, the
"bleeding edge" of development, you could say. New functionality gets added
to this version.
/*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see
* -mean-prec <int>
* The number of decimals after the decimal point for the mean.
* (default: 2)
*
*
*
* -stddev-prec <int>
* The number of decimals after the decimal point for the mean.
* (default: 2)
*
*
*
* -col-name-width <int>
* The maximum width for the column names (0 = optimal).
* (default: 0)
*
*
*
* -row-name-width <int>
* The maximum width for the row names (0 = optimal).
* (default: 25)
*
*
*
* -mean-width <int>
* The width of the mean (0 = optimal).
* (default: 0)
*
*
*
* -stddev-width <int>
* The width of the standard deviation (0 = optimal).
* (default: 0)
*
*
*
* -sig-width <int>
* The width of the significance indicator (0 = optimal).
* (default: 0)
*
*
*
* -count-width <int>
* The width of the counts (0 = optimal).
* (default: 5)
*
*
*
* -show-stddev
* Whether to display the standard deviation column.
* (default: no)
*
*
*
* -show-avg
* Whether to show the row with averages.
* (default: no)
*
*
*
* -remove-filter
* Whether to remove the classname package prefixes from the
* filter names in datasets.
* (default: no)
*
*
*
* -print-col-names
* Whether to output column names or just numbers representing them.
* (default: no)
*
*
*
* -print-row-names
* Whether to output row names or just numbers representing them.
* (default: no)
*
*
*
* -enum-col-names
* Whether to enumerate the column names (prefixing them with
* '(x)', with 'x' being the index).
* (default: no)
*
*
*
* -enum-row-names
* Whether to enumerate the row names (prefixing them with
* '(x)', with 'x' being the index).
* (default: no)
*
*
*
*
* @author FracPete (fracpete at waikato dot ac dot nz)
* @version $Revision: 10204 $
*/
public class ResultMatrixPlainText extends ResultMatrix {
/** for serialization. */
private static final long serialVersionUID = 1502934525382357937L;
/**
* initializes the matrix as 1x1 matrix.
*/
public ResultMatrixPlainText() {
this(1, 1);
}
/**
* initializes the matrix with the given dimensions.
*
* @param cols the number of columns
* @param rows the number of rows
*/
public ResultMatrixPlainText(int cols, int rows) {
super(cols, rows);
}
/**
* initializes the matrix with the values from the given matrix.
*
* @param matrix the matrix to get the values from
*/
public ResultMatrixPlainText(ResultMatrix matrix) {
super(matrix);
}
/**
* Returns a string describing the matrix.
*
* @return a description suitable for displaying in the experimenter gui
*/
@Override
public String globalInfo() {
return "Generates the output as plain text (for fixed width fonts).";
}
/**
* returns the name of the output format.
*
* @return the display name
*/
@Override
public String getDisplayName() {
return "Plain Text";
}
/**
* returns the default width for the row names.
*
* @return the width
*/
@Override
public int getDefaultRowNameWidth() {
return 25;
}
/**
* returns the default width for the counts.
*
* @return the width
*/
@Override
public int getDefaultCountWidth() {
return 5;
}
/**
* returns the header of the matrix as a string.
*
* @return the header
* @see #m_HeaderKeys
* @see #m_HeaderValues
*/
@Override
public String toStringHeader() {
int i;
int size;
String[][] data;
String result;
result = "";
// fill in data
data = new String[m_HeaderKeys.size()][2];
for (i = 0; i < m_HeaderKeys.size(); i++) {
data[i][0] = m_HeaderKeys.get(i).toString() + ":";
data[i][1] = m_HeaderValues.get(i).toString();
}
// pad
size = getColSize(data, 0);
for (i = 0; i < data.length; i++) {
data[i][0] = padString(data[i][0], size);
}
// build result
for (i = 0; i < data.length; i++) {
result += data[i][0] + " " + data[i][1] + "\n";
}
return result;
}
/**
* returns the matrix as plain text.
*
* @return the matrix
*/
@Override
public String toStringMatrix() {
StringBuffer result;
String[][] cells;
int i;
int j;
int n;
int k;
int size;
String line;
int indexBase;
StringBuffer head;
StringBuffer body;
StringBuffer foot;
int[] startMeans;
int[] startSigs;
int maxLength;
result = new StringBuffer();
head = new StringBuffer();
body = new StringBuffer();
foot = new StringBuffer();
cells = toArray();
startMeans = new int[getColCount()];
startSigs = new int[getColCount() - 1];
maxLength = 0;
// pad numbers
for (n = 1; n < cells[0].length; n++) {
size = getColSize(cells, n, true, true);
for (i = 1; i < cells.length - 1; i++) {
cells[i][n] = padString(cells[i][n], size, true);
}
}
// index of base column in array
indexBase = 1;
if (getShowStdDev()) {
indexBase++;
}
if (getShowStdDev()) {
}
// output data (without "(v/ /*)")
j = 0;
k = 0;
for (i = 1; i < cells.length - 1; i++) {
if (isAverage(i)) {
body.append(padString("", maxLength).replaceAll(".", "-") + "\n");
}
line = "";
for (n = 0; n < cells[0].length; n++) {
// record starts
if (i == 1) {
if (isMean(n)) {
startMeans[j] = line.length();
j++;
}
if (isSignificance(n)) {
startSigs[k] = line.length();
k++;
}
}
if (n == 0) {
line += padString(cells[i][n], getRowNameWidth());
if (!isAverage(i)) {
line += padString(
"(" + Utils.doubleToString(getCount(getDisplayRow(i - 1)), 0)
+ ")", getCountWidth(), true);
} else {
line += padString("", getCountWidth(), true);
}
} else {
// additional space before means
if (isMean(n)) {
line += " ";
}
// print cell
if (getShowStdDev()) {
if (isMean(n - 1)) {
if (!cells[i][n].trim().equals("")) {
line += "(" + cells[i][n] + ")";
} else {
line += " " + cells[i][n] + " ";
}
} else {
line += " " + cells[i][n];
}
} else {
line += " " + cells[i][n];
}
}
// add separator after base column
if (n == indexBase) {
line += " |";
}
}
// record overall length
if (i == 1) {
maxLength = line.length();
}
body.append(line + "\n");
}
// column names
line = padString(cells[0][0], startMeans[0]);
i = -1;
for (n = 1; n < cells[0].length; n++) {
if (isMean(n)) {
i++;
if (i == 0) {
line = padString(line, startMeans[i] - getCountWidth());
} else if (i == 1) {
line = padString(line, startMeans[i] - " |".length());
} else if (i > 1) {
line = padString(line, startMeans[i]);
}
if (i == 1) {
line += " |";
}
line += " " + cells[0][n];
}
}
line = padString(line, maxLength);
head.append(line + "\n");
head.append(line.replaceAll(".", "-") + "\n");
body.append(line.replaceAll(".", "-") + "\n");
// output wins/losses/ties
if (getColCount() > 1) {
line = padString(cells[cells.length - 1][0], startMeans[1] - 2, true)
+ " |";
i = 0;
for (n = 1; n < cells[cells.length - 1].length; n++) {
if (isSignificance(n)) {
line = padString(line,
startSigs[i] + 1 - cells[cells.length - 1][n].length());
line += " " + cells[cells.length - 1][n];
i++;
}
}
line = padString(line, maxLength);
} else {
line = padString(cells[cells.length - 1][0], line.length() - 2) + " |";
}
foot.append(line + "\n");
// assemble output
result.append(head.toString());
result.append(body.toString());
result.append(foot.toString());
return result.toString();
}
/**
* returns returns a key for all the col names, for better readability if the
* names got cut off.
*
* @return the key
*/
@Override
public String toStringKey() {
String result;
int i;
result = "Key:\n";
for (i = 0; i < getColCount(); i++) {
if (getColHidden(i)) {
continue;
}
result += LEFT_PARENTHESES + (i + 1) + RIGHT_PARENTHESES + " "
+ removeFilterName(m_ColNames[i]) + "\n";
}
return result;
}
/**
* returns the summary as string.
*
* @return the summary
*/
@Override
public String toStringSummary() {
String result;
String titles;
int resultsetLength;
int i;
int j;
if (m_NonSigWins == null) {
return "-summary data not set-";
}
result = "";
titles = "";
resultsetLength = 1 + Math.max(
(int) (Math.log(getColCount()) / Math.log(10)),
(int) (Math.log(getRowCount()) / Math.log(10)));
for (i = 0; i < getColCount(); i++) {
if (getColHidden(i)) {
continue;
}
titles += " "
+ Utils.padLeft("" + getSummaryTitle(i), resultsetLength * 2 + 3);
}
result += titles + " (No. of datasets where [col] >> [row])\n";
for (i = 0; i < getColCount(); i++) {
if (getColHidden(i)) {
continue;
}
for (j = 0; j < getColCount(); j++) {
if (getColHidden(j)) {
continue;
}
result += " ";
if (j == i) {
result += Utils.padLeft("-", resultsetLength * 2 + 3);
} else {
result += Utils.padLeft("" + m_NonSigWins[i][j] + " (" + m_Wins[i][j]
+ ")", resultsetLength * 2 + 3);
}
}
result += " | " + getSummaryTitle(i) + " = " + getColName(i) + '\n';
}
return result;
}
/**
* returns the ranking in a string representation.
*
* @return the ranking
*/
@Override
public String toStringRanking() {
int biggest;
int width;
String result;
int[] ranking;
int i;
int curr;
if (m_RankingWins == null) {
return "-ranking data not set-";
}
biggest = Math.max(m_RankingWins[Utils.maxIndex(m_RankingWins)],
m_RankingLosses[Utils.maxIndex(m_RankingLosses)]);
width = Math.max(2 + (int) (Math.log(biggest) / Math.log(10)),
">-<".length());
result = Utils.padLeft(">-<", width) + ' ' + Utils.padLeft(">", width)
+ ' ' + Utils.padLeft("<", width) + " Resultset\n";
ranking = Utils.sort(m_RankingDiff);
for (i = getColCount() - 1; i >= 0; i--) {
curr = ranking[i];
if (getColHidden(curr)) {
continue;
}
result += Utils.padLeft("" + m_RankingDiff[curr], width) + ' '
+ Utils.padLeft("" + m_RankingWins[curr], width) + ' '
+ Utils.padLeft("" + m_RankingLosses[curr], width) + ' '
+ removeFilterName(m_ColNames[curr]) + '\n';
}
return result;
}
/**
* Returns the revision string.
*
* @return the revision
*/
@Override
public String getRevision() {
return RevisionUtils.extract("$Revision: 10204 $");
}
/**
* for testing only.
*
* @param args ignored
*/
public static void main(String[] args) {
ResultMatrix matrix;
int i;
int n;
matrix = new ResultMatrixPlainText(3, 3);
// set header
matrix.addHeader("header1", "value1");
matrix.addHeader("header2", "value2");
matrix.addHeader("header2", "value3");
// set values
for (i = 0; i < matrix.getRowCount(); i++) {
for (n = 0; n < matrix.getColCount(); n++) {
matrix.setMean(n, i, (i + 1) * n);
matrix.setStdDev(n, i, ((double) (i + 1) * n) / 100);
if (i == n) {
if (i % 2 == 1) {
matrix.setSignificance(n, i, SIGNIFICANCE_WIN);
} else {
matrix.setSignificance(n, i, SIGNIFICANCE_LOSS);
}
}
}
}
System.out.println("\n\n--> " + matrix.getDisplayName());
System.out.println("\n1. complete\n");
System.out.println(matrix.toStringHeader() + "\n");
System.out.println(matrix.toStringMatrix() + "\n");
System.out.println(matrix.toStringKey());
System.out.println("\n2. complete with std deviations\n");
matrix.setShowStdDev(true);
System.out.println(matrix.toStringMatrix());
System.out.println("\n3. cols numbered\n");
matrix.setPrintColNames(false);
System.out.println(matrix.toStringMatrix());
System.out.println("\n4. second col missing\n");
matrix.setColHidden(1, true);
System.out.println(matrix.toStringMatrix());
System.out.println("\n5. last row missing, rows numbered too\n");
matrix.setRowHidden(2, true);
matrix.setPrintRowNames(false);
System.out.println(matrix.toStringMatrix());
System.out.println("\n6. mean prec to 3\n");
matrix.setMeanPrec(3);
matrix.setPrintRowNames(false);
System.out.println(matrix.toStringMatrix());
}
}
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