37 lines
1005 B
Python
37 lines
1005 B
Python
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import numpy as np
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def gauss_const(h):
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"""
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Returns the normalization constant for a gaussian
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"""
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return 1/(h*np.sqrt(np.pi*2))
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def gauss_exp(ker_x, xi, h):
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"""
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Returns the gaussian function exponent term
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"""
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num = - 0.5*np.square((xi- ker_x))
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den = h*h
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return num/den
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def kernel_function(h, ker_x, xi):
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"""
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Returns the gaussian function value. Combines the gauss_const and
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gauss_exp to get this result
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"""
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const = gauss_const(h)
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gauss_val = const*np.exp(gauss_exp(ker_x,xi,h))
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return gauss_val
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def weights(bw_manual, input_x, all_input_values ):
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w_row = []
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for x_i in all_input_values:
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ki = kernel_function(bw_manual, x_i, input_x)
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ki_sum = np.sum(kernel_function(bw_manual, all_input_values, input_x))
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w_row.append(ki/ki_sum)
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return w_row
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def single_y_pred(bw_manual, input_x, iks, igrek):
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w = weights(bw_manual, input_x, iks)
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y_single = np.sum(np.dot(igrek,w))
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return y_single
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