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Gram-Schmidt Orthogonization using Numpy
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import numpy as np | |
def gs_cofficient(v1, v2): | |
return np.dot(v2, v1) / np.dot(v1, v1) | |
def multiply(cofficient, v): | |
return map((lambda x : x * cofficient), v) | |
def proj(v1, v2): | |
return multiply(gs_cofficient(v1, v2) , v1) | |
def gs(X): | |
Y = [] | |
for i in range(len(X)): | |
temp_vec = X[i] | |
for inY in Y : | |
proj_vec = proj(inY, X[i]) | |
temp_vec = map(lambda x, y : x - y, temp_vec, proj_vec) | |
Y.append(temp_vec) | |
return Y | |
#Test data | |
test = np.array([[3.0, 1.0], [2.0, 2.0]]) | |
test2 = np.array([[1.0, 1.0, 0.0], [1.0, 3.0, 1.0], [2.0, -1.0, 1.0]]) | |
print np.array(gs(test)) | |
print np.array(gs(test2)) |
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