mhjensen
52 lines
1.4 KiB
Python
52 lines
1.4 KiB
Python
# Importing functions from folder with common functions for project 1
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import sys
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sys.path.append('../functions')
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from functions import *
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from regression import OLS
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn.linear_model import Ridge as OLS_sklearn
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# Making meshgrid of datapoints and compute Franke's function
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n = 3
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N = 1000
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x = np.sort(np.random.uniform(0, 1, N))
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y = np.sort(np.random.uniform(0, 1, N))
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x_mesh_, y_mesh_ = np.meshgrid(x,y)
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z = FrankeFunction(x_mesh_, y_mesh_)
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# Add noise
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z_noise = z + np.random.normal(scale = 1, size = (N,N))
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# Perform regression
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X = create_X(x_mesh_, y_mesh_, n=n)
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model = OLS()
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beta = model.fit(X, z_noise, ret=True)
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# Perform regression with Scikit learn using ridge with alpha = 0
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# Because of inconsistencies in linear_regression
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model2 = OLS_sklearn(alpha = 0, fit_intercept = False)
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model2.fit(X, np.ravel(z_noise))
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# Print beta-values of the two models
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print('============================')
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print('Calculated beta-values:', beta)
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print('Scikit-learn beta-values:', model2.coef_)
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# Create best-fit matrix for plotting
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x_r = np.linspace(0,1,N)
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y_r = np.linspace(0,1,N)
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x_mesh, y_mesh = np.meshgrid(x,y)
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X_r = create_X(x_mesh, y_mesh, n=n)
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# Predict
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z_reg = (model.predict(X_r)).reshape((N,N))
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plot_surface(x_mesh, y_mesh, z_reg, "OLS regression")
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print('============================ \n')
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print("MSE: %.5f" %MSE(z, z_reg))
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print("R2_Score: %.5f" %R2_Score(z, z_reg))
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