import pandas as pd
import numpy as np
from mylinearregression import MyLinearRegression as MyLR
import matplotlib.pyplot as plt
path = "ressources/day01/resources/"
data = pd.read_csv(path + "spacecraft_data.csv")
X = np.array(data[['Age', 'Thrust_power', 'Terameters']])
Y = np.array(data[['Sell_price']])
my_lreg = MyLR([1.0, 1.0, 1.0, 1.0])
my_lreg.theta = my_lreg.theta.reshape(-1, 1)
print(X.shape)
print(Y.shape)
print(my_lreg.theta.shape)
print(my_lreg.mse_(X, Y))
data.plot.scatter("Thrust_power", "Sell_price")
x = np.linspace(0, 200, 100)
y = my_lreg.theta[0] + (my_lreg.theta[2] * X[2])
# plt.plot(X[2], y, '-r', label='Linear model 1', color = "green")
plt.plot(x,
my_lreg.theta[0] + (my_lreg.theta[2] * x),
'-r',
label='Linear model 1',
color="red")
plt.legend(loc='upper left')
plt.grid()
plt.show(block=False)
# 144044.877...
import pandas as pd
import numpy as np
#from sklearn.metrics import mean_squared_error
from mylinearregression import MyLinearRegression as MyLR
from linear_model import linear_model, cost_model
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)
linear_model1 = MyLR(np.array([[0.0], [0.0]]))
linear_model1.theta = linear_model1.fit_(Xpill,
Yscore,
alpha=0.05,
n_cycle=1000)
Y_model1 = linear_model1.predict_(Xpill)
cost_elem = linear_model1.cost_(Xpill, Yscore)
#cost_model(linear_model1.theta, cost_elem)
linear_model(Xpill, Y_model1, Yscore)
print(linear_model1.theta)
#print(linear_model1.mse_(Yscore, Y_model1))
#print(mean_squared_error(Yscore, Y_model1))
#print(linear_model2.mse_(Yscore, Y_model2))
from mylinearregression import MyLinearRegression as MyLR import numpy as np X = np.array([[1., 1., 2., 3.], [5., 8., 13., 21.], [34., 55., 89., 144.]]) Y = np.array([[23.], [48.], [218.]]) mylr = MyLR([[1.], [1.], [1.], [1.], [1]]) print(mylr.predict_(X)) print(mylr.cost_elem_(X,Y)) print(mylr.cost_(X,Y)) mylr.theta = mylr.fit_(X, Y, alpha = 1.6e-4, n_cycle=100000) print(mylr.predict_(X)) print(mylr.cost_elem_(X,Y)) print(mylr.cost_(X,Y))
#cost function
linear4 = MyLR(np.array([[89.0], [-14]]))
theta0 = 89.0
fig = plt.figure()
axes = fig.add_axes([0.1, 0.1, 0.8, 0.8])
plt.title("Cost for different theta0")
plt.xlabel("theta1")
plt.ylabel("cost (j(theta0, theta1))")
#plt.axis([-14, 20, -3, 140])
n_samples = 6
colors = cm.rainbow(np.linspace(0, 1, n_samples))
for j in range(0, n_samples):
costs = []
for theta1 in range(-13, -2): #every curve
costs.append(linear4.cost_(Xpill, Yscore))
linear4.theta = np.array([[theta0], [theta1]])
theta0 += 1
print(costs)
plt.plot(range(-13, -2), costs, color=colors[j])
# print("------")
#plt.margins(0)
axes.set_xlim([-14, -4])
axes.set_ylim([20, 140])
plt.show()
#for i in range(-4, -14):
# plt.scatter(i, )