98 lines
2.7 KiB
Python
Executable File
98 lines
2.7 KiB
Python
Executable File
import numpy as np
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from numpy.core.fromnumeric import squeeze
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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 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_gauss(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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def epanechnikov_one(h, ker_x, xi):
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"""
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Returns the epanechnikov function value.
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"""
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value = 0.75*(1-np.square((xi-ker_x)/h))
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if (value < 0):
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value = 0
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return value
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def epanechnikov_list(h, ker_x, xi):
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"""
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Returns the epanechnikov function value.
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"""
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value = 0.75*(1-np.square((xi-ker_x)/h))
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value = [0 if i < 0 else i for i in value]
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return value
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def weights_epanechnikov(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 = epanechnikov_one(bw_manual, x_i, input_x)
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ki_sum = np.sum(epanechnikov_list(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_epanechnikov(bw_manual, input_x, x_values, y_values):
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w = weights_epanechnikov(bw_manual, input_x, x_values)
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y_single = np.sum(np.dot(y_values,w))
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return y_single
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def ker_reg(x_values, y_values, bw = 1, ker_fun = 'gauss'):
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ker_x = np.arange(0,40,0.1)
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if (ker_fun == 'gauss'):
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Y_pred = []
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for input_x in x_values:
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w = []
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Y_single = single_y_pred_epanechnikov(bw, input_x, x_values, y_values)
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Y_pred.append(Y_single)
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elif (ker_fun == 'epanechnikov'):
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Y_pred = []
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for input_x in x_values:
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w = []
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Y_single = single_y_pred_gauss(bw, input_x, x_values, y_values)
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Y_pred.append(Y_single)
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else:
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return 0
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return Y_pred |