import pandas as pd
import numpy as np
from normal_equation_model import graph_model
from mylinearregression import MyLinearRegression as MyLR
data = pd.read_csv("../resources/spacecraft_data.csv")
X_train = np.array(data[['Age', 'Thrust_power', 'Terameters']])
Y_train = np.array(data['Sell_price']).reshape(-1, 1)
myLR_ne = MyLR([[1.0], [1.0], [1.0], [1.0]])
myLR_lgd = MyLR([[1.0], [1.0], [1.0], [1.0]])
#Linear Gradient Descent
myLR_lgd.fit_(X_train, Y_train, alpha=5e-5, n_cycle=10000)
Y_pred_lgd = myLR_lgd.predict_(X_train)
print('COST Linear gradient descent:', myLR_lgd.cost_(X_train, Y_train))
#Normal Equation
myLR_ne.normalequation_(X_train, Y_train)
Y_pred_ne = myLR_ne.predict_(X_train)
print('COST Normal equation:', myLR_ne.cost_(X_train, Y_train))
graph_model(X_train, Y_train, Y_pred_lgd, Y_pred_ne)
import matplotlib.pyplot as plt
data = pd.read_csv("../resources/spacecraft_data.csv")
X = np.array(data[['Age', 'Thrust_power', 'Terameters']])
X1 = np.array(data[['Age', 'Thrust_power', 'Terameters']])
Y = np.array(data['Sell_price']).reshape(-1, 1)
theta1 = np.array([[1.], [1.], [1.], [1.]])
theta2 = np.array([[1.], [1.], [1.], [1.]])
myLR_ne = MyLR(theta1)
myLR_lgd = MyLR(theta2)
myLR_lgd.fit_(X, Y, alpha=5e-5, n_cycle=2000)
Y_new1 = myLR_lgd.predict_(X)
myLR_ne.normalequation_(X, Y)
print(myLR_ne.theta)
Y_new2 = myLR_ne.predict_(X)
'''
print("MSE = ")
print(myLR_lgd.theta)
print(myLR_lgd.mse_(Y, Y_new1))
print("MSE = ")
print(myLR_ne.theta)
print(myLR_ne.mse_(Y, Y_new2))
'''
plt.scatter(data.Age, Y_new1, color='green')
plt.scatter(data.Age, Y_new2, color='red')
plt.scatter(data.Age, Y, color='blue')
plt.title('Linear Regression vs. Normal Equation Comparaison')
plt.xlabel('Age en annee')
def draw_multi_regression(mylr):
#mylr.fit_()
mylr.predict_()
# Plot in function of age
fig, ax = plt.subplots()
ax.scatter(mylr.X[:, 0], mylr.Y)
ax.scatter(mylr.X[:, 0], mylr.Y_hat, c="blue")
plt.xlabel("Age")
plt.ylabel("Sell_price")
plt.title("")
fig.legend(loc="lower left")
plt.show()
plt.cla()
data = pd.read_csv("../resources/spacecraft_data.csv")
Y = np.array(data['Sell_price']).reshape(-1, 1)
X = np.array(data[['Age', 'Thrust_power', 'Terameters']])
myLR_ne = MyLR([1., 1., 1., 1.], X, Y)
myLR_lgd = MyLR([1., 1., 1., 1.], X, Y)
myLR_lgd.fit_(alpha=5e-5, n_cycle=10000)
myLR_ne.normalequation_()
print(myLR_lgd.mse_())
print(myLR_ne.mse_())
draw_multi_regression(myLR_ne)