# **************************************************************************** # import numpy as np import sys import matplotlib.pyplot as plt import pandas as pd from mylinearregression import MyLR data = pd.read_csv("./resources/spacecraft_data.csv") X = np.array(data[["Age", "Thrust_power", "Terameters"]]) Y = np.array(data[["Sell_price"]]) theta = np.array([[1.], [1.], [1.], [1.]]) mylr_ne = MyLR(theta) mylr_lgd = MyLR(theta) Y_pred = mylr_ne.predict_(X) ############### Gradient descente ############ print("Basic cost = " + str(mylr_lgd.mse_(X, Y))) mylr_lgd.fit_(X, Y, alpha=5e-5, n_cycle=10000) print("Cost after gradient descente = " + str(mylr_lgd.mse_(X, Y))) print("Theta after gradient descente = " + str(mylr_lgd.theta)) Y_grad = mylr_lgd.predict_(X) ############################################## #print() ############# Normale Equation ############### mylr_ne.normalequation_(X, Y) print("Cost after Normale equation = " + str(mylr_ne.mse_(X, Y))) print("Theta after normale equation = " + str(mylr_ne.theta))
# **************************************************************************** # # # # ::: :::::::: # # so_much_hyp.py :+: :+: :+: # # +:+ +:+ +:+ # # By: ythomas <*****@*****.**> +#+ +:+ +#+ # # +#+#+#+#+#+ +#+ # # Created: 2020/02/12 13:18:07 by ythomas #+# #+# # # Updated: 2020/02/12 15:10:23 by ythomas ### ########.fr # # # # **************************************************************************** # import numpy as np import matplotlib.pyplot as plt from mylinearregression import MyLR import pandas as pd data = pd.read_csv("./resources/saturn_asteroids.csv") X = np.array(data[['x1', 'x2']]) X2 = np.array(data[['x1**2', 'x2**2']]) Y = np.array(data[['y']]) hypo1 = MyLR([1., 1.]) hypo2 = MyLR([1., 1.]) print(hypo1.predict_(X)) print(hypo2.predict_(X2)) #hypo1.fit_(X[:,0], Y, alpha = 1e-4, n_cycle = 1e5) #hypo2.fit_(X[:,1], Y, alpha = 1e-4, n_cycle = 1e5) #hypo1.rmse_(X[:,0],Y) #hypo2.rmse_(X[:,0],Y)
# +#+#+#+#+#+ +#+ # # Created: 2020/02/10 12:22:13 by ythomas #+# #+# # # Updated: 2020/02/10 15:13:21 by ythomas ### ########.fr # # # # **************************************************************************** # import numpy as np from mylinearregression import MyLR import sys import matplotlib.pyplot as plt import pandas as pd data = pd.read_csv("./resources/are_blue_pills_magics.csv") Xpill = np.array(data["Micrograms"]).reshape(-1, 1) Yscore = np.array(data["Score"]).reshape(-1, 1) theta1 = np.array([[89.0], [-8]]) theta2 = np.array([[89.0], [-6]]) mylm1 = MyLR(theta1) mylm2 = MyLR(theta2) print(Xpill) print("========") print(Yscore) Y_lm1 = mylm1.predict_(Xpill) plt.plot(Xpill, Yscore, 'g^') y = Xpill * theta1[1] + theta1[0] plt.plot(Xpill, y) plt.grid(True) plt.show() print(mylm1.mse_(Xpill, Yscore)) print(mylm2.mse_(Xpill, Yscore))
#print(mylr.theta) theta_age = np.array([[1000.0], [-1.0]]) theta_thrust = np.array([[0.], [-1.0]]) theta_tera = np.array([[800.0], [-1.0]]) Xage2 = np.hstack((Xage, np.full((Xage.shape[0], 1), 1))) mylr_age = MyLR(theta_age) mylr_thrust = MyLR(theta_thrust) mylr_tera = MyLR(theta_tera) plt.subplot(231) plt.ylabel('y : sell price') plt.xlabel('x1 : age') plt.plot(Xage, Yprice, 'bo') pred_age = mylr_age.predict_(Xage) plt.plot(Xage, pred_age, 'm.') plt.grid('True') plt.subplot(232) plt.ylabel('y : sell price') plt.xlabel('x2 : Thrust Power') plt.plot(Xthrust, Yprice, 'go') pred_thrust = mylr_thrust.predict_(Xthrust) plt.plot(Xthrust, pred_thrust, 'r.') plt.grid('True') plt.subplot(233) plt.ylabel('y : sell price') plt.xlabel('x3 : Terameters') plt.plot(Xtera, Yprice, 'yo')