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Numpy Cheat sheet
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import numpy as np | |
def run(): | |
# create a column vector | |
col_vec = np.array([[1], [2]]) | |
print "column vector" | |
print col_vec | |
# create a row vector | |
row_vec = np.array([[1, 2]]) | |
print "row vector" | |
print row_vec | |
# create a matrix | |
mat = np.array([[1, 2], [3, 4]]) | |
print "matrix" | |
print mat | |
# inspect dimensions | |
print "row vector dimensions", row_vec.ndim | |
shape = row_vec.shape | |
print "row vector rows", shape[0], "columns", shape[1] | |
print "matrix dimensions", mat.ndim | |
shape = mat.shape | |
print "matrix rows", shape[0], "columns", shape[1] | |
# transpose | |
vec_t = row_vec.transpose() # or row_vec.T | |
print "transposed vector" | |
print vec_t | |
mat_t = mat.transpose() # or mat.T | |
print "transposed matrix" | |
print mat_t | |
a = np.array([[2], [-4], [1]]) | |
b = np.array([[2], [1], [-2]]) | |
# addition | |
print "a + b" | |
print a + b | |
# subtraction | |
print "a - b" | |
print a - b | |
# scalar multiplication | |
print "1.2 * a" | |
print 1.2 * a | |
# element wise multiplication | |
print "a * b" | |
print a * b | |
# vector scalar product | |
print "a . b" | |
print np.dot(a.transpose(), b) | |
# vector cross product | |
print "a x b" | |
print np.cross(a, b, axis=0) # or np.cross(a.T, b.T).T | |
identity = np.array([[1, 0], [0, 1]]) | |
# matrix vector product | |
print "identity . col_vec" | |
print np.dot(identity, col_vec) | |
# matrix product | |
print "identity . mat" | |
print np.dot(identity, mat) | |
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