added codes

2019-11-17 22:22:03 +01:00
parent 3623cd187b
commit e8967bfd3f
6 changed files with 4773 additions and 0 deletions
@@ -0,0 +1,51 @@
+# Importing functions from folder with common functions for project 1
+import sys
+sys.path.append('../functions')
+from functions import *
+from regression import OLS
+
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.linear_model import Ridge as OLS_sklearn
+
+
+
+# Making meshgrid of datapoints and compute Franke's function
+n = 3
+N = 1000
+x = np.sort(np.random.uniform(0, 1, N))
+y = np.sort(np.random.uniform(0, 1, N))
+x_mesh_, y_mesh_ = np.meshgrid(x,y)
+z = FrankeFunction(x_mesh_, y_mesh_)
+
+# Add noise
+z_noise = z + np.random.normal(scale = 1, size = (N,N))
+
+# Perform regression
+X = create_X(x_mesh_, y_mesh_, n=n)
+model = OLS()
+beta = model.fit(X, z_noise, ret=True)
+
+# Perform regression with Scikit learn using ridge with alpha = 0
+# Because of inconsistencies in linear_regression
+model2 = OLS_sklearn(alpha = 0, fit_intercept = False)
+model2.fit(X, np.ravel(z_noise))
+
+# Print beta-values of the two models
+print('============================')
+print('Calculated beta-values:', beta)
+print('Scikit-learn beta-values:', model2.coef_)
+
+# Create best-fit matrix for plotting
+x_r = np.linspace(0,1,N)
+y_r = np.linspace(0,1,N)
+x_mesh, y_mesh = np.meshgrid(x,y)
+X_r = create_X(x_mesh, y_mesh, n=n)
+
+# Predict
+z_reg = (model.predict(X_r)).reshape((N,N))
+plot_surface(x_mesh, y_mesh, z_reg, "OLS regression")
+print('============================ \n')
+print("MSE: %.5f" %MSE(z, z_reg))
+print("R2_Score: %.5f" %R2_Score(z, z_reg))
+