Below is the syntax highlighted version of matrix.py
from §2.2 Modules and Clients.
#----------------------------------------------------------------------- # matrix.py #----------------------------------------------------------------------- import sys import stdarray import random # A bare-bones collection of static methods for manipulating matrices. #----------------------------------------------------------------------- # Return an m-by-n matrix containing random float values between # 0 and 1. def rand(m, n): randMatrix = stdarray.create2D(m, n, 0.0) for i in range(m): for j in range(n): randMatrix[i][j] = random.random() return randMatrix #----------------------------------------------------------------------- # Return an n-by-n identity matrix. def identity(n): identityMatrix = stdarray.create2D(n, n, 0.0) for i in range(n): identityMatrix[i][i] = 1.0 return identityMatrix #----------------------------------------------------------------------- # Return the number which is the dot product of vectors v1 and v2. def dot(v1, v2): v1Length = len(v1) if v1Length != len(v2): raise Exception('v1 length must equal v2 length') dotProd = 0.0 for i in range(v1Length): dotProd += v1[i] * v2[i] return dotProd #----------------------------------------------------------------------- # Return the matrix which is the transpose of square matrix m. def transpose(m): rowCount = len(m) colCount = len(m[0]) if rowCount != colCount: raise Exception('row count must equal col count') transposeMatrix = stdarray.create2D(rowCount, colCount, 0.0) for row in range(rowCount): for col in range(colCount): transposeMatrix[col][row] = m[row][col] return transposeMatrix #----------------------------------------------------------------------- # Return the matrix which is the sum of matrices m1 and m2. def add(m1, m2): rowCount = len(m1) colCount = len(m1[0]) if rowCount != len(m2): raise Exception('m1 row count must equal m2 row count') if colCount != len(m2[0]): raise Exception('m1 col count must equal m2 col count') total = stdarray.create2D(rowCount, colCount, 0.0) for row in range(rowCount): for col in range(colCount): total[row][col] = m1[row][col] + m2[row][col] return total #----------------------------------------------------------------------- # Return the matrix which is the difference of matrices m1 and m2. def subtract(m1, m2): rowCount = len(m1) colCount = len(m1[0]) if rowCount != len(m2): raise Exception('m1 row count must equal m2 row count') if colCount != len(m2[0]): raise Exception('m1 col count must equal m2 col count') diff = stdarray.create2D(rowCount, colCount, 0.0) for row in range(rowCount): for col in range(colCount): diff[row][col] = m1[row][col] - m2[row][col] return diff #----------------------------------------------------------------------- # Return the matrix which is the product of matrices m1 and m2. def multiplyMM(m1, m2): m1RowCount = len(m1) m1ColCount = len(m1[0]) m2RowCount = len(m2) m2ColCount = len(m2[0]) if m1ColCount != m2RowCount: raise Exception('m1 col count must equal m2 row count') prod = stdarray.create2D(m1RowCount, m2ColCount, 0.0) for i in range(m1RowCount): for j in range(m2ColCount): for k in range(m1ColCount): prod[i][j] += m1[i][k] * m2[k][j] return prod #----------------------------------------------------------------------- # Return the vector which is the matrix-vector product of matrix m # and vector v. def multiplyMV(m, v): mRowCount = len(m) mColCount = len(m[0]) vLength = len(v) if mColCount != vLength: raise Exception('m col count must equal v length') prod = stdarray.create1D(mRowCount, 0.0) for i in range(mRowCount): for j in range(mColCount): prod[i] += m[i][j] * v[j] return prod #----------------------------------------------------------------------- # Return the vector which is the vector-matrix product of vector v # and matrix m. def multiplyVM(v, m): vLength = len(v) mRowCount = len(m) mColCount = len(m[0]) if vLength != mRowCount: raise Exception('v length must equal m row count') prod = stdarray.create1D(mColCount, 0.0) for j in range(mColCount): for i in range(mRowCount): prod[j] += m[i][j] * v[i] return prod #----------------------------------------------------------------------- # For testing. def main(): import stdio stdio.writeln('A') stdio.writeln('--------------------') a = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] stdarray.write2D(a) stdio.writeln() stdio.writeln('B') stdio.writeln('--------------------') b = identity(5) stdarray.write2D(b) stdio.writeln() stdio.writeln('C') stdio.writeln('--------------------') c = rand(5, 5) stdarray.write2D(c) stdio.writeln() stdio.writeln('A^T') stdio.writeln('--------------------') at = transpose(a); stdarray.write2D(b) stdio.writeln() stdio.writeln('A + A^T') stdio.writeln('--------------------') d = add(a, at) stdarray.write2D(d) stdio.writeln() stdio.writeln('A * A^T') stdio.writeln('--------------------') e = multiplyMM(a, at) stdarray.write2D(e) stdio.writeln() # stdio.writeln('--------------------') # stdio.writeln('--------------------') # moves = int(sys.argv[1]) # p = stdarray.readFloat2D() # ranks = stdarray.create1D(len(p), 0.0) # ranks[0] = 1.0 # for i in range(moves): # ranks = multiplyVM(ranks, p) # stdarray.write1D(ranks) if __name__ == '__main__': main() #----------------------------------------------------------------------- # python matrix.py # A # -------------------- # 3 3 # 1 2 3 # 4 5 6 # 7 8 9 # # B # -------------------- # 5 5 # 1.0 0.0 0.0 0.0 0.0 # 0.0 1.0 0.0 0.0 0.0 # 0.0 0.0 1.0 0.0 0.0 # 0.0 0.0 0.0 1.0 0.0 # 0.0 0.0 0.0 0.0 1.0 # # C # -------------------- # 5 5 # 0.37627444331077653 0.32586357815639067 0.23540064982343156 0.6899813748554937 0.5286982582636341 # 0.28992687465780576 0.5629957065410093 0.45781423848968006 0.8903057550676357 0.5601631173759734 # 0.6785301245100017 0.11651814401724558 0.8763789354440634 0.032291377717908465 0.32928407619813105 # 0.6173798692131891 0.7550840799583114 0.3348102933789717 0.705621812503534 0.09121764056242698 # 0.1803750228194455 0.5135643692698265 0.09450902697153707 0.5250451696720445 0.7724263231251629 # A^T # -------------------- # 5 5 # 1.0 0.0 0.0 0.0 0.0 # 0.0 1.0 0.0 0.0 0.0 # 0.0 0.0 1.0 0.0 0.0 # 0.0 0.0 0.0 1.0 0.0 # 0.0 0.0 0.0 0.0 1.0 # # A + A^T # -------------------- # 3 3 # 2 6 10 # 6 10 14 # 10 14 18 # # A * A^T # -------------------- # 3 3 # 14.0 32.0 50.0 # 32.0 77.0 122.0 # 50.0 122.0 194.0 #