def main():
if len(argv) < 4:
print "Usage: <num eigenvectors> <tolerance> <text file>"
return
data = load_matrix(argv[3])
k = int(argv[1])
epsilon = float(argv[2])
rows = len(data)
cols = len(data[0])
A = Matrix(rows, cols, data)
res, vals = power_method(k, epsilon, A)
Vecs = Matrix(rows, k)
for i in range(rows):
for j in range(k):
Vecs.data[i][j] = res[j].data[i][0]
f1 = open('vecs.txt','w')
f2 = open('vals.txt','w')
# TODO fix formatting
for i in range(rows):
for j in range(k):
f1.write(str(Vecs.data[i][j]) + ' ')
f1.write('\n')
for i in range(k):
f2.write(str(vals[i]) + '\n')
f1.close()
f2.close()
def main():
if len(argv) < 4:
print "Usage: <num eigenvectors> <tolerance> <text file>"
return
data = load_matrix(argv[3])
k = int(argv[1])
epsilon = float(argv[2])
rows = len(data)
cols = len(data[0])
A = Matrix(rows, cols, data)
U, S, V = svd(k, epsilon, A)
f1 = open("U.txt", "w")
f2 = open("V.txt", "w")
f3 = open("S.txt", "w")
# output U
for line in U.data:
for val in line:
f1.write(str(val) + " ")
f1.write("\n")
# output V
for line in V.data:
for val in line:
f2.write(str(val) + " ")
f2.write("\n")
# output S
for val in S:
f3.write(str(val) + "\n")
f1.close()
f2.close()
f3.close()
# Code to test Frobenius Norm
print "Frobenius norm:", frob_norm(A, U, S, V)
