// FILE. . . . . d:/hak/hlt/src/hlt/math/matrix/sources/Matrix.java
// EDIT BY . . . Hassan Ait-Kaci
// ON MACHINE. . Hak-Laptop
// STARTED ON. . Fri Nov 15 13:09:37 2019
/**
* @version Last modified on Wed Dec 04 13:33:48 2019 by hak
* @author Hassan Aït-Kaci
* @copyright © by the author
*/
package hlt.math.matrix;
import hlt.language.tools.Misc;
/**
* This implements basic two-dimensional linear algebra in the form of a
* collection of operations defined on a Matrix class
* represented as a 2D array of doubles.
*
*
* Contents
*
*
*
* @see SquareMatrix
*/
public class Matrix extends GenMatrix
{
/**
*
*/
/**
* Constructs a vacuous Matrix.
*/
protected Matrix ()
{
}
/**
* Constructs a rows-by-cols Matrix of
* 0.0's.
*/
public Matrix (int rows, int cols)
{
if (rows <= 0 || cols <= 0)
throw new RuntimeException("Illegal matrix dimensions: ("+
rows+","+cols+")");
this.rows = rows;
this.cols = cols;
data = new double[rows][cols];
}
/**
* Constructs a new Matrix copying the given data
* array.
*/
public Matrix (double[][] data)
{
this(data,false);
}
/**
* Constructs a new Matrix sharing the given data
* array if inPlace is true, and copying it
* otherwise.
*/
public Matrix (double[][] data, boolean inPlace)
{
if (inPlace)
setData(data);
else
this.data = copyData(data);
}
/**
* Constructs a new Matrix from the data array of the given
* Matrix.
*/
protected Matrix (Matrix M)
{
this(M.data);
}
/**
*
*/
/**
* The number of rows of this Matrix.
*/
protected int rows;
/**
* The number of columns of this Matrix.
*/
protected int cols;
/**
* This is the rows-by-cols array containing the
* entries of this Matrix.
*/
protected double[][] data;
/**
*
*/
/**
* Return the number of rows of this Matrix.
*/
public final int rows ()
{
return rows;
}
/**
* Return the number of columns of this Matrix.
*/
public final int cols ()
{
return cols;
}
/**
* Return the data array of this Matrix.
*/
public final double[][] data ()
{
return data;
}
/**
*
*/
/**
* Set the (i,j) entry of this Matrix to the
* given value and returns the previous value that was
* there. Throws a RuntimeException for indices out of bounds.
* N.B.: Matrix indices are counted from 1.
*/
public final double set (int i, int j, double value)
{
// check for legal entry and adjust i and j to start from 0 in data in
// the code following this
checkLegalEntry(i--,j--);
return safeDataUpdate(i,j,truncate(value));
}
/**
* Set the (row,col) entry of this Matrix
* data array to the given value and returns the
* previous value that was there. N.B. This counts row
* and col from 0 and assumes indexing is safe
* (e.g., after checkLegalEntry).
*/
protected final double safeDataUpdate (int row, int col, double value)
{
double old = data[row][col];
data[row][col] = value;
return old;
}
/**
* Return the value of in this Matrix are entry indices
* (i,j). Throws a RuntimeException for
* indices out of bounds. N.B.: Matrix indices are counted from
* 1.
*/
public final double get (int i, int j)
{
// check for legal entry and adjust i and j to start from 0 in data in
// the code following this
checkLegalEntry(i--,j--);
return data[i][j];
}
/**
* Modifies this matrix to have the given two-dimensional data array
* of doubles and corresponding numbers of rows and columns.
* If the array null a runtime exception is thrown.
*/
public Matrix setData (double[][] data)
{
if (data == null)
throw new RuntimeException("Attempt to initialize a matrix with a null data array");
rows = data.length;
cols = data[0].length;
this.data = data;
return this;
}
/**
*
*/
/**
* Return a new Matrix equal to this+M.
*/
public Matrix plus (Matrix M)
{
verifyCompatibleDimension(M.rows,M.cols,rows,cols);
Matrix result = new Matrix(rows,cols);
for (int row = 0; row < rows; row++)
for (int col = 0; col < cols; col++)
result.data[row][col] = data[row][col]+M.data[row][col];
return result;
}
/**
* Modifies in place the entries of this to those of
* this plus M and returns this.
*/
public Matrix i_plus (Matrix M)
{
verifyCompatibleDimension(M.rows,M.cols,rows,cols);
for (int row = 0; row < rows; row++)
for (int col = 0; col < cols; col++)
data[row][col] += M.data[row][col];
return this;
}
/**
* Return a new Matrix equal to this times
* M (does not modify this).
*/
public Matrix times (Matrix M)
{
if (cols != M.rows)
throw new RuntimeException("Cannot multiply a "+rows+"x"+cols+
" matrix by a "+M.rows+"x"+M.cols+" matrix");
Matrix result = new Matrix(rows,M.cols);
for (int row = 0; row < rows; row++)
for (int col = 0; col < M.cols; col++)
{
double entry = 0.0;
for (int k = 0; k < cols; k++)
entry += data[row][k]*M.data[k][col];
// N.B: since multiplying two truncated entries may exceed the
// truncation limit, the resulting matrix entry must be truncated
result.data[row][col] = truncate(entry);
}
return result;
}
/**
* Modifies in place the entries of this to those of
* this times M and returns this.
*/
public Matrix i_times (Matrix M)
{
return setData(this.times(M).data);
}
/**
* Return a new Matrix equal to this-M.
*/
public Matrix minus (Matrix M)
{
verifyCompatibleDimension(M.rows,M.cols,rows,cols);
Matrix result = new Matrix(rows,cols);
for (int row = 0; row < rows; row++)
for (int col = 0; col < cols; col++)
result.data[row][col] = data[row][col]-M.data[row][col];
return result;
}
/**
* Modifies this Matrix to this-M and returns
* this
*/
public Matrix i_minus (Matrix M)
{
verifyCompatibleDimension(M.rows,M.cols,rows,cols);
for (int row = 0; row < rows; row++)
for (int col = 0; col < cols; col++)
data[row][col] -= M.data[row][col];
return this;
}
/**
* Return a new Matrix equal to -this.
*/
public Matrix minus ()
{
Matrix result = new Matrix(rows,cols);
for (int row = 0; row < rows; row++)
for (int col = 0; col < cols; col++)
result.data[row][col] = -data[row][col];
return result;
}
/**
* Modifies this Matrix to -M and returns
* this
*/
public Matrix i_minus ()
{
for (int row = 0; row < rows; row++)
for (int col = 0; col < cols; col++)
data[row][col] = -data[row][col];
return this;
}
/**
* Return a new Matrix each entry of which is equal the
* corresponding entry in this multiplied by scale.
*/
public Matrix scale (double factor)
{
Matrix result = new Matrix(rows,cols);
for (int row = 0; row < rows; row++)
for (int col = 0; col < cols; col++)
// N.B: since multiplying a truncated entry by a double may
// exceed the truncation limit, the resulting matrix entry must
// be truncated
result.data[row][col] = truncate(factor*data[row][col]);
return result;
}
/**
* Modifies this Matrix to factor*this and returns
* this
*/
public Matrix i_scale (double factor)
{
for (int row = 0; row < rows; row++)
for (int col = 0; col < cols; col++)
// N.B: since multiplying a truncated entry by a double may
// exceed the truncation limit, the resulting matrix entry must
// be truncated
data[row][col] = truncate(factor*data[row][col]);
return this;
}
/**
* Return true iff this matrix and M are
* entry-wise equal.
*/
public boolean equal (Matrix M)
{
verifyCompatibleDimension(M.rows,M.cols,rows,cols);
for (int row = 0; row < rows; row++)
for (int col = 0; col < cols; col++)
if (data[row][col] != M.data[row][col])
return false;
return true;
}
/**
* Swap rows row and col of this
* Matrix.
*/
public void swapRows (int row, int col)
{
double[] tmp = data[row];
data[row] = data[col];
data[col] = tmp;
}
/**
* Swap columns row and col of this
* Matrix.
*/
public void swapCols (int row, int col)
{
double tmp;
for (int k = 0; k < data.length; k++)
{
tmp = data[k][row];
data[k][row] = data[k][col];
data[k][col] = tmp;
}
}
/**
* Return a new Matrix that is the transpose of
* this.
*/
public Matrix transpose ()
{
Matrix M = new Matrix(cols,rows);
for (int row = 0; row < rows; row++)
for (int col = 0; col < cols; col++)
M.data[col][row] = data[row][col];
return M;
}
/**
*
*/
/**
* Return true iff this matrix has no data array.
*/
public final boolean isVacuous ()
{
return data == null;
}
/**
* Return true iff this Matrix is square.
*/
public boolean isSquare ()
{
return (rows == cols);
}
/**
* Return true iff this Matrix is
* degenerate (i.e., one of its rows or columns has only
* 0.0).
*/
public boolean isDegenerate ()
{
return (degenerateRow() != -1) || (degenerateCol() != -1);
}
/**
* Return the row index of a row that contains only 0.0 if
* there exists one; -1 otherwise.
*/
public int degenerateRow ()
{
for (int row = 0; row < rows; row++)
{
int col = 0;
boolean rowIsAllZero = true;
while (rowIsAllZero && col < cols)
{
rowIsAllZero = (data[row][col] == 0.0);
col++;
}
if (rowIsAllZero)
return row;
}
return -1;
}
/**
* Return the column index of a column that contains only
* 0.0 if there exists one; -1 otherwise.
*/
public int degenerateCol ()
{
for (int col = 0; col < cols; col++)
{
int row = 0;
boolean colIsAllZero = true;
while (colIsAllZero && row < rows)
{
colIsAllZero = (data[row][col] == 0.0);
row++;
}
if (colIsAllZero)
return col;
}
return -1;
}
/**
* Return true iff all entries are 0.0.
*/
public boolean isZeroMatrix ()
{
for (int row = 0; row < rows; row++)
for (int col = 0; col < cols; col++)
if (data[row][col] != 0.0)
return false;
return true;
}
/* ************************************************************************ */
/**
* Return true iff this is a row-stochastic matrix;
* i.e., all the rows are in [0.0,1.0] and their
* components add up to 1.0.
*/
public final boolean isRowStochastic ()
{
return isRowStochastic(data);
}
/**
* Modifies this non-negative matrix in place so that all its
* rows have non-negative entries that add up to 1. It does
* so by normalizing each entry by dividing it by the sum of all the
* entries on its row.
*/
public final void makeRowStochastic ()
{
for (int row = 0; row < rows; row++)
normalizeRow(row);
}
/**
* Return true iff this is a column-stochastic matrix;
* i.e., all the columns are in [0.0,1.0] and their
* components add up to 1.0.
*/
public final boolean isColumnStochastic ()
{
return isColumnStochastic(data);
}
/**
* Return true iff this is a doubly-stochastic matrix;
* i.e., that it all the rows and all the columns have only
* entries in [0.0,1.0] that add up to 1.0 in each
* dimension.
*/
public final boolean isDoublyStochastic ()
{
return isDoublyStochastic(data);
}
/**
* Return true iff the given data array this is a
* row-stochastic matrix; i.e., all the rows are in
* [0.0,1.0] and their components add up to 1.0.
*/
protected final boolean isRowStochastic (double[][] data)
{
int rows = data.length;
int cols = data[0].length;
for (int row = 0; row < rows; row++)
{
double rowSum = 0;
for (int col = 0; col < cols; col++)
{
if (data[row][col] < 0.0 || data[row][col] > 1.0)
return false; // not within [0.0,1.0]
rowSum += data[row][col];
}
if (truncate(rowSum) != 1.0)
return false;
}
// ok - we're good
return true;
}
/**
* Return true iff the given data array is
* column-stochastic; i.e., all the columns are in
* [0.0,1.0] and their components add up to 1.0.
*/
protected final boolean isColumnStochastic (double[][] data)
{
int rows = data.length;
int cols = data[0].length;
for (int col = 0; col < cols; col++)
{
double colSum = 0;
for (int row = 0; row < rows; row++)
{
if (data[row][col] < 0.0 || data[row][col] > 1.0)
return false; // not within [0.0,1.0]
colSum += data[row][col];
}
if (truncate(colSum) != 1.0)
return false;
}
// ok - we're good
return true;
}
/**
* Return true iff this is a doubly-stochastic data array;
* i.e., that it all the rows and all the columns have only
* entries in [0.0,1.0] that add up to 1.0 in each
* dimension.
*/
protected final boolean isDoublyStochastic (double[][] data)
{
int rows = data.length;
int cols = data[0].length;
// as we compute for each row the sum of its entries we also
// accumulate the sum on entries for each column and store it in
// this array until we reach the last row
double[] colSums = new double[rows];
for (int row = 0; row < rows; row++)
{
double rowSum = 0;
for (int col = 0; col < cols; col++)
{
if (data[row][col] < 0.0 || data[row][col] > 1.0)
return false; // not within [0.0,1.0]
rowSum += data[row][col];
colSums[row] += data[row][col];
}
if (rowSum != 1.0)
return false;
}
// if we're here, we need to check each column sums
for (int row = 0; row < rows; row++)
if (truncate(colSums[row]) != 1.0)
return false;
// ok - we're good
return true;
}
/**
* Modifies the row-th row of this Matrix by
* normalizing each of its entries by dividing it by the sum of all
* the entries on this row. N.B.: This does not works due to
* rounding issue (see truncate).
*/
private final void normalizeRow (int row)
{
double rowSum = 0.0; // the sum of the entries on this row
System.err.println();
System.err.println("------------------------------------------------------------------------");
System.err.println("Row sum ["+row+"]:");
System.err.println("------------------------------------------------------------------------");
System.err.println();
for (int col = 0; col < cols; col++)
{
data[row][col] = truncate(data[row][col]);
if (data[row][col] < 0.0)
throw new RuntimeException
("Negative data array entry "+data[row][col]+" at indices "+
row+","+col+": can only make non-negative matrix row-stochastic.");
System.err.print(" ");
System.err.printf(floatFormatString(),data[row][col]);
System.err.print(col<(cols-1)?"+":"");
rowSum += data[row][col];
}
rowSum = truncate(rowSum);
System.err.println(" ---> rowSum["+row+"] = "+rowSum);
if (rowSum == 0.0)
return; // then there is nothing to do
// needed to sum the normalized row entries to offset rounding errors
double normalizedRowSum = 0.0;
double actualNormalizedRowSum = 0.0;
for (int col = 0; col < cols; col++)
{
if (data[row][col] == rowSum)
{ // this means that the row has only one non-zero at col
// so the normalized value at col is 1.00 (and all
// others still 0.0)
data[row][col] = normalizedRowSum = actualNormalizedRowSum = 1.0;
return;
}
// dividing by the sum is safe since != 0.0:
double normalizedEntry = truncate(data[row][col]/rowSum);
System.err.print(" ");
System.err.printf(floatFormatString(),normalizedEntry);
System.err.print(col<(cols-1)?"+":"");
if (col == lastNonZeroColInRow(row))
{ // this is the last non-zero in this row: adjust to how
// close to 1.0: i.e., 1.0 - sum of all the previous non-0
// normalized entries!
data[row][col] = 1.0 - actualNormalizedRowSum;
actualNormalizedRowSum += normalizedEntry;
normalizedRowSum = 1.0;
}
else // otherwise, just normalize the entry:
{
actualNormalizedRowSum += normalizedEntry;
data[row][col] = normalizedEntry;
}
}
System.err.println(" ---> actualNormalizedRowSum["+row+"] = "+actualNormalizedRowSum);
// System.err.println(" ---> normalizedRowSum["+row+"] = "+normalizedRowSum);
System.err.println();
if (truncate(actualNormalizedRowSum) == 1.0)
System.err.println("Actual normalized sum of row["+row+
"] is close enough to 1.0: so row["+row+
"] is deemed row-stochastic");
else
System.err.println("Actual normalized sum of row["+row+
"] is NOT close enough to 1.0: so row["+row+
"] is NOT row-stochastic");
System.err.println("------------------------------------------------------------------------");
}
/**
* Return the column index of the first non-zero entry in the given
* row, or -1 if there is none (i.e., the row
* contains only zero entries).
*/
public final int firstNonZeroColInRow (int row)
{
int col = 0;
while (col < cols && data[row][col] == 0.0)
col++;
return (col == cols) ? -1 : col;
}
/**
* Return the row index of the first non-zero entry in the given
* col, or -1 if there is none (i.e., the
* column contains only zero entries).
*/
public final int firstNonZeroRowInColumn (int col)
{
int row = 0;
while (row < rows && data[row][row] == 0.0)
row++;
return (row == rows) ? -1 : row;
}
/**
* Return the column index of the last non-zero entry in the given
* row, or -1 if there is none (i.e., the row
* contains only zero entries).
*/
public final int lastNonZeroColInRow (int row)
{
int col = cols-1;
while (col > 0 && data[row][col] == 0.0)
col--;
return col;
}
/**
* Return the row index of the last non-zero entry in the given
* col, or -1 if there is none (i.e., the
* column contains only zero entries).
*/
public final int lastNonZeroRowInColumn (int col)
{
int row = rows-1;
while (row > 0 && data[row][row] == 0.0)
row--;
return row;
}
/**
*
*
* Following are some methods concerning one-dimensional matrices;
* i.e., (row or column) vectors.
*/
/**
* Return true iff this is a one-dimensional
* Matrix.
*/
public boolean isVector ()
{
return isRowVector() || isColVector();
}
/**
* Return true iff this is a one-row
* Matrix.
*/
public boolean isRowVector ()
{
return rows == 1;
}
/**
* Return true iff this is a one-colum
* Matrix.
*/
public boolean isColVector ()
{
return cols == 1;
}
/**
*
*/
/**
* Prints this matrix to standard output printing each double
* entry formatted using the default
* floatFormat format string.
*/
public void show ()
{
for (int row = 0; row < rows; row++)
{
for (int col = 0; col < cols; col++)
System.out.printf(floatFormatString(),data[row][col]);
System.out.println();
}
}
/**
*
*/
/**
* Print an empty line on the standard output.
*/
static void ln ()
{
Misc.ln();
}
/**
* Print n empty lines on the standard output.
*/
static void ln (int n)
{
Misc.ln(n);
}
/**
* Print a line not ending in a CR on the standard output.
*/
static void jot (String what)
{
Misc.jot(what);
}
/**
* Print a line ending in a CR on the standard output.
*/
static void say (String what)
{
Misc.say(what);
}
/**
* Print a line ending in a CR and skip a line on the standard output.
*/
static void sln (String what)
{
Misc.sln(what);
}
/**
*
*
*
* The following are various (static) useful methods handy when using
* Matrix.
*/
/**
* Return a new double[][] that is equal to the given array
* data.
*/
static public final double[][] copyData (double[][] data)
{
int rows = data.length;
int cols = data[0].length;
double[][] dataCopy = new double[rows][cols];
for (int row = 0; row < rows; row++)
for (int col = 0; col < cols; col++)
dataCopy[row][col] = data[row][col];
return dataCopy;
}
/**
* Return a new identity SquareMatrix of order
* order.
*/
static public SquareMatrix identity (int order)
{
return SquareMatrix.identity(order);
}
/**
*
*
*
* The following are various (static) methods that are used in various
* places to verify the consistency of various Matrix
* parameters in a given context.
*/
/**
* Verifies the compatibility of the given dimensions and throws an
* IncompatibleMatrixDimensionException if it is violated.
*/
static final protected void verifyCompatibleDimension (int rows, int cols, int otherRows, int otherCols)
{
if (rows != otherRows || cols != otherCols)
throw new IncompatibleMatrixDimensionException(rows,cols,otherRows,otherCols);
}
/**
* Throws a RuntimeException if there is no such entry at
* indices (i,j) in this Matrix. N.B.:
* Matrix indices are counted from 1.
*/
final protected void checkLegalEntry (int i, int j)
{
checkLegalIndex(i,data.length);
checkLegalIndex(j,data[0].length);
}
/**
* Throws a RuntimeException if index i is not between
* 1 and size inclusive. N.B.: Matrix indices are
* counted from 1.
*/
static final protected void checkLegalIndex (int i, int size)
{
if (i < 1 || i > size)
throw new RuntimeException
("Matrix index "+i+" out of bounds [1,"+size+"]");
}
/**
* Throws a RuntimeException if any entry in the given
* data array is not stochastic.
*/
static final protected void checkForStochasticEntries (double[][] data)
{
for (int row = 0; row < data.length; row++)
for (int col = 0; col < data[0].length; col++)
if (data[row][col] < 0.0 || data[row][col] > 1.0)
throw new RuntimeException
("Non-stochastic entry in data array at (row,col): ("+row+","+col+")");
}
/**
*
*
*
* The following are various (static) paraphernalia for the control of
* printing the double entries of a Matrix object.
*/
/**
* Default print width used to format
* doubles and doubles using Java's System.out.printf
* format syntax. In order to control the format of entries printed
* by the show() method, redefine this string to the wished
* format string.
*/
static private int defaultPrintWidth = 9;
/**
* Return the default print width for double and double
* types.
*/
static public final int defaultPrintWidth ()
{
return defaultPrintWidth;
}
/**
* Sets the total print width currently effective for double and
* double to be used in the printf format string to the
* specified width. This cannot be less that 3 (if so,
* it is set to 3).
*/
static public final int setDefaultPrintWidth (int width)
{
// %[width].[precision]f
return defaultPrintWidth = Math.max(3,width);
}
/**
* Resets to 9 the total print width currently effective for
* double and double to be used in the printf
* format string.
*/
static public final int resetDefaultPrintWidth ()
{
return setDefaultPrintWidth(9);
}
/**
* Return the current double format string in
* effect for printing double and double values using
* printf.
*/
public static String floatFormatString ()
{
return
"%" +
Integer.toString(defaultPrintWidth) +
"." +
Integer.toString(currentSignificantDigits()) +
"f ";
}
/**
*
*
*
* The following are various (static) paraphernalia for the control of
* double precision when dealing with random values.
*/
/**
* Return a double[rows][cols] with entries equal to random
* values between 0.0 and 1.0 inclusive.
*/
static public double[][] randomDataArray (int rows, int cols)
{
double[][] data = new double[rows][cols];
for (int row = 0; row < rows; row++)
for (int col = 0; col < cols; col++)
data[row][col] = randomValue();
return data;
}
/**
* Return a double[rows][cols] with entries equal to random
* values between 0.0 and scale inclusive.
*/
static public double[][] randomDataArray (int rows, int cols, double scale)
{
double[][] data = new double[rows][cols];
for (int row = 0; row < rows; row++)
for (int col = 0; col < cols; col++)
data[row][col] = randomValue(scale);
return data;
}
/**
* Toss a coin and returns either false or
* true. This can be biased by setting
* COIN_TOSS_BIAS to any double between
* [0.0,1.0]. The lower the bias makes coin tossing return
* false; the higher, it makes it return true. If
* 1, then coin-toss is always true. If 0,
* then coin-toss is always false. The default is
* COIN_TOSS_BIAS = 0.5.
*/
static public final boolean headsOrTail ()
{
if (Math.random() < COIN_TOSS_BIAS)
return true;
return false;
}
/**
* The default COIN_TOSS_BIAS.
*/
static private double COIN_TOSS_BIAS = 0.5;
/**
* Return the current toss-coin bias (a double in
* [0.0,1.0]).
*/
static public final double bias ()
{
return COIN_TOSS_BIAS;
}
/**
* Set the toss-coin bias to bias. The lower the bias makes
* coin tossing return false more often; the higher, it makes
* it return true more often. If 1, then coin-toss
* is always true. If 0, then coin-toss is always
* false.
*/
static public final double setBias (double bias)
{
return COIN_TOSS_BIAS = Math.max(0,Math.min(1,bias));
}
/**
* Reset the toss-coin bias to 0.5.
*/
static public final double resetBias ()
{
return COIN_TOSS_BIAS = 0.5;
}
/**
* Return the result of truncating value to the value obtained
* by setting to 0 all its digits past the position indicated by
* currentSignificantDigits().
*/
static public final double truncate (double value)
{
return value; // NO OP UNTIL I FIGURE OUT HOW TO CONTROL THIS DAMN THING!
// double scale = Math.pow(10,currentSignificantDigits());
// return Math.floor(value*scale)/scale; // this means always <= value!
// // return round(Math.floor(value*scale)/scale); // see below
}
/**
* Do the same kind of rounding that we do for values that are too close
* (within current threshold θ) of 0.0 or
* 1.0, but to any value in
* [θ,1-θ] closest (within current threshold
* θ).
* e.g.: if threshold θ = 0.01 and digits =
* 3 (i.e., precision = 0.001), the double
* value 0.123456 is rounded downwards to 0.123 because:
* ...
* N.B.: this not well defined unless we justify why
* we round to the closest value at a precision equal to
* 10\*precision!
*/
static private final double round (double value)
{ // finish later; does nothing and return argument for now
return value;
}
/**
* Return the given array data after of truncating all its
* entries by setting to 0 all its digits past the position
* indicated by currentSignificantDigits().
*/
static public final double[][] truncate (double[][] data)
{
if (data == null)
throw new RuntimeException("Vacuous data array");
for (int row = 0; row < data.length; row++)
for (int col = 0; col < data[0].length; col++)
data[row][col] = truncate(data[row][col]);
return data;
}
/**
* The greatest number of significant decimal digits accepted for a
* matrix entries. It is set by default to 5 decimal places.
*/
static private int MAX_SIGNIFICANT_DIGITS = 5;
/**
* The method maxSignificantDigits() returns the currently
* effective number of significant decimal digits after the dot that are
* taken into account.
* N.B.: maxSignificantDigits() is the next integer
* higher than -log10(currentPrecision()); i.e., in
* Java:
*
* maxSignificantDigits() == -Math.ceil(Math.log10(currentPrecision()))
* For example, if currentPrecision() = 0.0001, then
* log10currentPrecision() = -4, i.e.,
* maxSignificantDigits() = 4.
* To keep all this inter-dependencies this consistent, the maximum
* number of significant digits is the only constant that can be reset
* and all others are computed from it.
*/
static public final int maxSignificantDigits ()
{
return MAX_SIGNIFICANT_DIGITS;
}
/**
* The method setMaxSignificantDigits() sets the maximum number
* of significant decimal digits after the dot that are taken into
* account to the maximum of 1 and digits and returns
* it.
*/
static public final int setMaxSignificantDigits (int digits)
{
return MAX_SIGNIFICANT_DIGITS = Math.max(1,digits);
}
/**
* How many significant decimal digits are currently taken into account
* for matrix entries (all digits after this one are 0). This
* defaults to 1.
*/
static private int CURRENT_SIGNIFICANT_DIGITS = 1;
/**
* Return the current number of significant of decimal digits after the
* dot that are taken into account.
*/
public static int currentSignificantDigits ()
{
return CURRENT_SIGNIFICANT_DIGITS;
}
/**
* The method setCurrentSignificantDigits(int) sets the current
* number of significant decimal digits dot to the given int if
* it is between 1 and maxSignificantDigits(), or
* whichever of these that is closest otherwise, and returns it.
*/
static public final int setCurrentSignificantDigits (int digits)
{
return CURRENT_SIGNIFICANT_DIGITS = Math.min(Math.max(1,digits),maxSignificantDigits());
}
/**
* The method currentPrecision()
* returns the precision currently in effect.
* N.B.: It is always equal to
* 10-currentSignificantDigits(); i.e., in
* Java, Math.pow(10,-d).
* For example, if currentSignificantDigits() = 3, then
* currentPrecision() = 0.001.
*/
static public final double currentPrecision ()
{
return Math.pow(10.0,-CURRENT_SIGNIFICANT_DIGITS);
}
/**
*
*
*
* The following are various (static) paraphernalia for the control of
* how close to 0.0 or 1.0 some random values
* generated by the randomValue() method
* should be rounded downwards to 0.0 or upwards to
* 1.0 according a coin toss. (See the methods that control coin-toss bias.)
*/
/**
* This is a value in [0.0,1.0] used as approximation threshold
* to 0.0 if too close; defaults to 0.0.
*/
static private double THRESHOLD0 = 0.0;
/**
* Return the threshold for random rounding approximation to
* 0.0 currently in effect.
*/
static public final double threshold0 ()
{
return THRESHOLD0;
}
/**
* This is a value in [0.0,1.0] used as approximation threshold
* to 1.0 if too close; defaults to 0.0.
*/
static private double THRESHOLD1 = 0.0;
/**
* Return the threshold for random rounding approximation to
* 1.0 currently in effect.
*/
static public final double threshold1 ()
{
return THRESHOLD1;
}
/**
* Sets the current approximation threshold to 0.0 to the given
* double in [0.0,1.0] used as approximation
* threshold to round downwards to 0.0 if too close.
*/
static public double setThreshold0 (double threshold)
{
return THRESHOLD0 = Math.min(Math.max(0.0,threshold),1.0);
}
/**
* Resets the current approximation threshold to 0.0 its
* default value (0.0).
*/
static public double resetThreshold0 ()
{
return THRESHOLD0 = 0.0;
}
/**
* Sets the current approximation threshold to 1.0 to the
* given double in [0.0,1.0] used as approximation
* threshold to round upwards to 1.0 if too close.
*/
static public double setThreshold1 (double threshold)
{
return THRESHOLD1 = Math.min(Math.max(0.0,threshold),1.0);
}
/**
* Resets the current approximation threshold to 1.0 to its
* default value (0.0).
*/
static public double resetThreshold1 ()
{
return THRESHOLD1 = 0.0;
}
/**
* Resets the current approximation thresholds to 0.0 and 1 to their
* default value (0.0).
*/
static public void resetThresholds ()
{
resetThreshold0();
resetThreshold1();
}
/**
* The static method randomValue()
* returns a random double picked in the closed
* interval [0.0,1.0] (i.e., including 0.0
* and 1.0), modulo the currently effective number of decimal
* digits and precision. The int digits (=
* 1, 2, etc,...) is how many decimal digits
* after the decimal point doubles are approximated.
*/
static private double randomValue ()
{
// pick a random double in [0.0,1.0)
double picked = truncate(Math.random());
// the above returns a double in [0.0,1.0): it does not include 1.0
// or may me too small to be worth distinguishing from 0.0; so
// we round it to either 0.0 or 1.0 if too close to either
double threshold0 = threshold0();
double threshold1 = threshold1();
if (picked < threshold0) /* when picked is too close to 0.0 according to threshold0 */
return
headsOrTail() ? picked : /* toss a coin and return either what was picked or */
0.0; /* return 0.0 (round downwards) */
if ((1-picked) < threshold1) /* when picked is too close to 1.0 according to threshold1 */
return
headsOrTail() ? picked : /* toss a coin and return either what was picked or */
1.0; /* return 1.0 (round upwards) */
// otherwise return picked as is
return picked;
}
/**
* Show the random-number generation and doubleing-point precision
* control parameters currently in effect.
*/
static public void showParameters ()
{
ln();
say("--------------------------------------------------------------------");
say("Matrix class parameter settings:") ;
say("--------------------------------------------------------------------");
jot("Significant number of digits = "+Matrix.currentSignificantDigits());
say(" (i.e., current precision = "+Matrix.currentPrecision()+")");
say("Current double and double printf print width = "+Matrix.defaultPrintWidth());
say("Current threshold to 0.0 = "+Matrix.threshold0());
say("Current threshold to 1.0 = "+Matrix.threshold1());
say("Current coin-toss bias = "+Matrix.bias());
say("--------------------------------------------------------------------");
}
/**
* The static method randomValue(double scale) returns a
* random double picked in the closed interval
* [0.0,scale] if scale is positive, or
* [scale,0.0] if scale is negative, modulo the
* currently effective number of decimal digits and precision.
*/
static private double randomValue (double scale)
{
return truncate(scale*randomValue());
}
/**
* Create and return a random rows-by-cols matrix with
* entries in [0.0,1.0].
*/
public static Matrix random (int rows, int cols)
{
return new Matrix().setData(randomDataArray(rows,cols));
}
/**
* Create and return a random rows-by-cols matrix with
* entries in [0.0,scale].
*/
public static Matrix random (int rows, int cols, double scale)
{
return new Matrix().setData(randomDataArray(rows,cols,scale));
}
}