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s434695 2021-06-01 16:30:04 +02:00
parent 7723b52935
commit 4ee3bf5ec8
2 changed files with 49 additions and 57 deletions
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22
KernelRegression.py
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@ -15,14 +15,6 @@ def gauss_exp(ker_x, xi, h):
den = h*h den = h*h
return num/den return num/den
def gauss_exp(ker_x, xi, h):
"""
Returns the gaussian function exponent term
"""
num = - 0.5*np.square((xi- ker_x))
den = h*h
return num/den
def kernel_function(h, ker_x, xi): def kernel_function(h, ker_x, xi):
""" """
Returns the gaussian function value. Combines the gauss_const and Returns the gaussian function value. Combines the gauss_const and
@ -48,7 +40,7 @@ def single_y_pred_gauss(bw_manual, input_x, iks, igrek):
def epanechnikov_one(h, ker_x, xi): def epanechnikov_one(h, ker_x, xi):
""" """
Returns the epanechnikov function value. Returns the epanechnikov function value for one element.
""" """
value = 0.75*(1-np.square((xi-ker_x)/h)) value = 0.75*(1-np.square((xi-ker_x)/h))
if (value < 0): if (value < 0):
@ -57,7 +49,7 @@ def epanechnikov_one(h, ker_x, xi):
def epanechnikov_list(h, ker_x, xi): def epanechnikov_list(h, ker_x, xi):
""" """
Returns the epanechnikov function value. Returns the epanechnikov function value for list of elements.
""" """
value = 0.75*(1-np.square((xi-ker_x)/h)) value = 0.75*(1-np.square((xi-ker_x)/h))
value = [0 if i < 0 else i for i in value] value = [0 if i < 0 else i for i in value]
@ -78,19 +70,19 @@ def single_y_pred_epanechnikov(bw_manual, input_x, x_values, y_values):
return y_single return y_single
def ker_reg(x_values, y_values, bw = 1, ker_fun = 'gauss'): def ker_reg(x_values, y_values, bw = 1, ker_fun = 'gauss'):
"""
ker_x = np.arange(0,40,0.1) x_values, y_values, bw = 1, ker_fun = 'gauss'
ker_fun = 'gauss' or 'epanechnikov'
returns Y_pred
"""
if (ker_fun == 'gauss'): if (ker_fun == 'gauss'):
Y_pred = [] Y_pred = []
for input_x in x_values: for input_x in x_values:
w = []
Y_single = single_y_pred_epanechnikov(bw, input_x, x_values, y_values) Y_single = single_y_pred_epanechnikov(bw, input_x, x_values, y_values)
Y_pred.append(Y_single) Y_pred.append(Y_single)
elif (ker_fun == 'epanechnikov'): elif (ker_fun == 'epanechnikov'):
Y_pred = [] Y_pred = []
for input_x in x_values: for input_x in x_values:
w = []
Y_single = single_y_pred_gauss(bw, input_x, x_values, y_values) Y_single = single_y_pred_gauss(bw, input_x, x_values, y_values)
Y_pred.append(Y_single) Y_pred.append(Y_single)
else: else:

84
kernel_regression.ipynb
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