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37 lines
1.0 KiB
Python
37 lines
1.0 KiB
Python
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# numpy matrix operations
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# sr 12/01/2013
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import numpy
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ary1 = numpy.array([1,2,3,4,5]) # must be same type
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ary2 = numpy.zeros((3,4)) # 3x4 matrix consisiting of 0s
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ary3 = numpy.ones((3,4)) # 3x4 matrix consisiting of 1s
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ary4 = numpy.identity(3) # 3x3 identity matrix
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ary5 = ary1.copy() # make a copy of ary1
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item1 = ary3[0, 0] # item in row1, column1
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ary2.shape # tuple of dimensions. Here: (3,4)
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ary2.size # number of elements. Here: 12
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ary2_t = ary2.transpose() # transposes matrix
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ary2.ravel() # makes an array linear (1-dimensional)
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# by concatenating rows
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ary2.reshape(2,6) # reshapes array (must have same dimensions)
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ary3[0:2, 0:3] # submatrix of first 2 rows and first 3 columns
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ary3 = ary3[[2,0,1]] # re-arrange rows
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# element-wise operations
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ary1 + ary1
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ary1 * ary1
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numpy.dot(ary1, ary1) # matrix/vector (dot) product
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numpy.sum(ary1) # sums up all elements in the array
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numpy.mean(ary1) # average of all elements in the array
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