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))
Y_ne = mylr_ne.predict_(X)
##############################################
# +#+#+#+#+#+ +#+ #
# 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))