def main():
data = pd.read_csv("./data/data.csv")
x = np.array(data["km"]).reshape(-1, 1)
y = np.array(data["price"]).reshape(-1, 1)
lr = MyLR(np.zeros(x.shape[1] + 1), alpha=1e-3, max_iter=10000)
# Standardisation
lr.setup_zscore(x)
# Before training
print("Starting cost:", lr.cost_(x, y), end="\n\n")
plot_all(x, y, lr)
# Training model
cost_history = lr.fit_(x, y, get_cost=True)
# Plotting cost evolution
plt.plot(cost_history, "m-")
plt.show()
# After training
print("Ending cost:", lr.cost_(x, y))
print("Thetas: ", lr.thetas, end="\n\n")
plot_all(x, y, lr)
# Save config in a file
params = lr.get_params_()
with open("config.mlr", "w") as f:
f.write(repr(params))
print("-> config.mlr created")
def main():
data = pd.read_csv("are_blue_pills_magics.csv")
Xpill = np.array(data["Micrograms"]).reshape(-1, 1)
Yscore = np.array(data["Score"]).reshape(-1, 1)
mlr = MyLinearRegression([[1.0], [1.0]])
mlr.fit_(Xpill, Yscore, alpha=0.005, n_cycle=2000)
best_theta = mlr.theta
print(best_theta)
better_y_score = mlr.predict_(Xpill)
print(better_y_score)
plt.title(
"Evolution of the space driving score in function of the quantity of blue pill (in micrograms). \nIn blue the real values and in green the predicted values.",
fontsize=8)
plt.xlabel("micrograms")
plt.ylabel("Score")
plt.plot(Xpill, Yscore, 'bo', Xpill, better_y_score, 'g-.', Xpill,
better_y_score, "go")
plt.show()
plt.close()
linear_model1 = MyLinearRegression(np.array([[89.0], [-6]]))
linear_model2 = MyLinearRegression(np.array([[89.0], [-6]]))
Y_model1 = linear_model1.predict_(Xpill)
Y_model2 = linear_model2.predict_(Xpill)
print(linear_model1.mse_(Y_model1, Yscore))
# 57.60304285714282
print(mean_squared_error(Y_model1, Yscore))
# 57.603042857142825
print(linear_model2.mse_(Y_model2, Yscore))
print("cost = ", linear_model2.cost_(Xpill, Yscore))
# 232.16344285714285
print(mean_squared_error(Y_model2, Yscore))
# # 232.16344285714285
x = []
y = [
] # On a créé deux listes vides pour contenir les abscisses et les ordonnées
move_theta = -14.0
for i in range(1000): # On veut les points de 0 à 100
linear_model1 = MyLinearRegression(np.array([[88.0], [move_theta]]))
# for i in range(5):
Y_model1 = linear_model1.predict_(Xpill)
y.append(float(linear_model1.cost_(Xpill, Yscore)))
x.append(float(linear_model1.theta[1]))
move_theta += 0.01
# linear_model1.fit_(Xpill, Yscore, alpha = 0.00005, n_cycle=10)
plt.plot(x, y)
plt.show()
plt.close()
def plot_model(x, y, data, theta, alpha=0.001, max_iter=100000):
X = np.array(data[["{}".format(x)]])
Y = np.array(data[["{}".format(y)]])
myLR_obj = MyLR(theta, alpha, max_iter)
print(myLR_obj.cost_(Y, myLR_obj.predict_(X)))
myLR_obj.fit_(X[:, 0].reshape(-1, 1), Y)
Y_model = myLR_obj.predict_(X)
print(myLR_obj.cost_(Y, Y_model))
plt.plot(X, Y_model, 'gs')
plt.plot(X, Y_model, 'g--', label="Spredict(pills)")
plt.plot(X, Y, 'bo', label="Strue")
plt.grid(True)
plt.legend(loc='upper right', bbox_to_anchor=(0.33, 1.15))
plt.ylabel("Space driving score")
plt.xlabel("Quantity of blue pill (in micrograms)")
plt.show()
def MultiLinearRegression(X, Y, alpha, n_cycle): X_train = np.array(data[X]) Y_train = np.array(data[['Sell_price']]) my_lreg = MyLR([[1.0], [1.0], [1.0], [1.0]]) my_lreg.fit_(X_train,Y_train, alpha, n_cycle) Y_pred = my_lreg.predict_(X_train) print(my_lreg.cost_(X_train, Y_train)) linear_model(X_train, Y_train, Y_pred)
def SingleLinearRegression(X, Y, alpha, n_cycle): X_train = np.array(data[X]).reshape(-1,1) Y_train = np.array(data[Y]).reshape(-1,1) myLR_age = MyLR([[1000.0], [-1.0]]) myLR_age.fit_(X_train, Y_train, alpha, n_cycle) Y_pred = myLR_age.predict_(X_train) print(myLR_age.cost_(X_train, Y_train)) linear_model(X_train, Y_train, Y_pred)
def get_poly_cost(x, y, poly, alpha=0.001, n_cycle=1000, thetas=None):
if thetas is None:
thetas = [1] * (poly + 1)
lr = MyLR(thetas, alpha=alpha, n_cycle=n_cycle)
poly_x = add_polynomial_features(x, poly)
lr.fit_(poly_x, y)
cost = lr.cost_(poly_x, y)
print(f"Poly {poly}: {cost} | {repr(lr.thetas)}")
return cost
def test_MyLinearRegressing():
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]], 5e-5, 320000)
# Example 0:
print(mylr.predict_(X), end="\n\n")
# Output:
# array([[8.], [48.], [323.]])
# Example 1:
print(mylr.cost_elem_(X, Y), end="\n\n")
# Output:
# array([[37.5], [0.], [1837.5]])
# Example 2:
print(mylr.cost_(X, Y), end="\n\n")
# Output:
# 1875.0
# Example 3:
mylr.fit_(X, Y)
print(mylr.thetas, end="\n\n")
# Output:
# array([[18.023..], [3.323..], [-0.711..], [1.605..], [-0.1113..]])
# Example 4:
print(mylr.predict_(X), end="\n\n")
# Output:
# array([[23.499..], [47.385..], [218.079...]])
# Example 5:
print(mylr.cost_elem_(X, Y), end="\n\n")
# Output:
# array([[0.041..], [0.062..], [0.001..]])
# Example 6:
print(mylr.cost_(X, Y), end="\n\n")
def compare_polynomials(x: np.ndarray, y: np.ndarray, i: int) -> None:
theta = [2.5] * (x.shape[1] + 1)
alpha = 1e-8
max_iter = int(1e+6)
linear_model = MyLR(theta, alpha, max_iter)
x_train, x_test, y_train, y_test = data_spliter(x, y, 0.6)
linear_model.fit_(x_train, y_train)
y_hat = linear_model.predict_(x_test)
this_cost = linear_model.cost_(y_test, y_hat)
print(i, this_cost)
print(linear_model.thetas)
plt.bar(i, this_cost, label="$%d_{th} cost: %.3f$" % (i, this_cost))
def draw_cost_function(mylr):
fig, ax = plt.subplots() #renvoie une figure et des axes
t0 = mylr.theta[0]
thetas_0 = [t0 - 20, t0 - 10, t0, t0 + 10, t0 + 20]
for theta_0 in thetas_0:
theta = np.linspace(-14, 4, 100).reshape(100, 1)
theta = np.insert(theta, 0, theta_0, axis=1)
y = np.array([])
for i in range(theta.shape[0]):
tmp_lr = MyLR(theta[i].reshape(2, 1), mylr.X, mylr.Y)
dot = tmp_lr.cost_()
y = np.append(y, dot)
plt.plot(theta[:, 1], y)
plt.xlabel("Theta1")
plt.ylabel("Cost function J(Theta0, Theta1")
plt.title(
"Evolution of the cost function J in fuction of Theta0 for different values of Theta1"
)
plt.show()
plt.cla()
# Output: # array([[10.74695094], # [17.05055804], # [24.08691674], # [36.24020866], # [42.25621131]]) # Example 0.1: print(lr1.cost_elem_(lr1.predict_(x), y)) # Output: # array([[77.72116511], # [49.33699664], # [72.38621816], # [37.29223426], # [78.28360514]]) # Example 0.2: print(lr1.cost_(lr1.predict_(x), y)) # Output: # 315.0202193084312 # Example 1.0: lr2 = MyLR([0, 0]) lr2.fit_(x, y) print(lr2.thetas) # Output: # array([[1.40709365], # [1.1150909 ]]) # Example 1.1: print(lr2.predict_(x)) # Output: # array([[15.3408728 ], # [25.38243697], # [36.59126492],
import numpy as np from mylinearregression import MyLinearRegression as MyLR 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]]) res = mylr.predict_(X) print(res) res = mylr.cost_elem_(X, Y) print(res) res = mylr.cost_(X, Y) print(res) res = mylr.fit_(X, Y, alpha=1.6e-4, n_cycle=200000) print(res) res = mylr.predict_(X) print(res) res = mylr.cost_elem_(X, Y) print(res) res = mylr.cost_(X, Y) print(res)
var = temp
std = math.sqrt(var / (len(x) - 1))
return (x - mu) / std
csv_data = pd.read_csv("../resources/are_blue_pills_magics.csv")
y_n = []
x = np.array(csv_data["Micrograms"]).reshape(-1, 1)
x = minmax(x)
y = np.array(csv_data["Score"]).reshape(-1, 1)
y = minmax(y)
plt.scatter(x, y)
x9 = add_polynomial_features(x, 9)
# mylr4 = MyLR([[10.0],[-21.0 ], [-0.28], [4.63], [6.73]],alpha=5e-3)
mylr9 = MyLR(
[[0.99549772], [-3.04228406], [11.0342294], [-12.5192794], [-7.56251887],
[4.59267205], [9.57475922], [5.99224473], [-1.55560663], [-7.52630899]],
alpha=0.55)
mylr9.fit_(x9, y)
print(mylr9.cost_(x9, y))
continuous_x = np.arange(0, 1, 0.001).reshape(-1, 1)
x_9 = add_polynomial_features(continuous_x, 9)
y_hat = mylr9.predict_(x_9)
print(mylr9.thetas)
# print(x_9)
# print(y)
plt.plot(continuous_x, y_hat, color='orange')
plt.show()
plt.show() print(linear_model.mse_(Y_model_age, Y)) X = np.array(data[['Age', 'Thrust_power', 'Terameters']]) my_lreg = MyLR([[1000.0], [0.0], [1000.0], [1.0]]) my_lreg.fit_(X, Y, alpha=0.00005, n_cycle=125) Y_multi_model = my_lreg.predict_(X) plt.subplot(131) data_plot = plt.plot(X1, Y, 'o', color='darkblue', label='Sell price') plt.plot(X1, Y_multi_model, 'c.') plt.grid() plt.subplot(132) data_plot = plt.plot(X2, Y, 'go', label='Sell price') plt.plot(X2, Y_multi_model, '.', color='lime') plt.grid() plt.subplot(133) data_plot = plt.plot(X3, Y, 'mo', label='Sell price') plt.plot(X3, Y_multi_model, '.', color='violet') plt.grid() plt.show() print(my_lreg.cost_(Y_multi_model, Y))
print(myLR_obj.cost_(Y, Y_model))
plt.plot(X, Y_model, 'gs')
plt.plot(X, Y_model, 'g--', label="Spredict(pills)")
plt.plot(X, Y, 'bo', label="Strue")
plt.grid(True)
plt.legend(loc='upper right', bbox_to_anchor=(0.33, 1.15))
plt.ylabel("Space driving score")
plt.xlabel("Quantity of blue pill (in micrograms)")
plt.show()
data = pd.read_csv("spacecraft_data.csv")
#plot_model("Age", "Sell_price", data, [[1000.0], [-1.0]], alpha = 2.5e-5, max_iter = 10000)
#plot_model("Thrust_power", "Sell_price", data, [[0], [10.0]], alpha = 1e-4, max_iter = 10000)
#plot_model("Terameters", "Sell_price", data, [[1000.0], [-1.0]], alpha = 1e-4, max_iter = 50000)
X = np.array(data[['Age', 'Thrust_power', 'Terameters']])
Y = np.array(data[["Sell_price"]])
myLR_obj = MyLR([1.0, 1.0, 1.0, 1.0], alpha=5e-10, max_iter=10000)
print(myLR_obj.cost_(Y, myLR_obj.predict_(X)))
myLR_obj.fit_(X, Y)
Y_model = myLR_obj.predict_(X)
print(myLR_obj.cost_(Y, Y_model))
plt.plot(X[:, 2], Y_model, 'go', label="Spredict(pills)")
plt.plot(X[:, 2], Y, 'bo', label="Strue")
plt.grid(True)
plt.legend(loc='upper right', bbox_to_anchor=(0.33, 1.15))
plt.ylabel("Space driving score")
plt.xlabel("Quantity of blue pill (in micrograms)")
plt.show()
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sys
sys.path.append("../ex07")
from mylinearregression import MyLinearRegression as MyLR
sys.path.append("../ex10")
from polinomial_model import add_polynomial_features
data = pd.read_csv("are_blue_pills_magics.csv")
X = np.array(data["Micrograms"]).reshape(-1, 1)
Y = np.array(data["Score"]).reshape(-1, 1)
myLR_obj = MyLR([90.0, -1.0, 1.0, 2.0], alpha=5e-6, max_iter=20000)
print(myLR_obj.cost_(Y, add_polynomial_features(X, 3)))
myLR_obj.fit_(add_polynomial_features(X, 3), Y)
Y_model = myLR_obj.predict_(add_polynomial_features(X, 3))
print(myLR_obj.cost_(Y, Y_model))
plt.plot(X, Y_model, 'go', label="Spredict(pills)")
plt.plot(X, Y, 'bo', label="Strue")
plt.grid(True)
plt.legend(loc='upper right', bbox_to_anchor=(0.33, 1.15))
plt.ylabel("Space driving score")
plt.xlabel("Quantity of blue pill (in micrograms)")
plt.show()
return x
data = pd.read_csv("../resources/spacecraft_data.csv")
X = np.array(data[['Age','Thrust_power','Terameters']])
Y = np.array(data[['Sell_price']])
reg = MLR([1.0, 1.0, 1.0, 1.0])
# power = 1
# reg.fit_(X,Y, alpha = 6.5e-5, n_cycle = 1000000)
# print("cost ", power, " : ", reg.cost_(X, Y), "\n")
power = 2
reg.thetas = np.full(3 ** power + 1, 1.0)
P = add_polynomial_features(X, power)
reg.fit_(P, Y, alpha = 1e-9, n_cycle = 5000)
print("cost ", power, " : ", reg.cost_(P, Y), "\n")
# power = 3
# reg.thetas = np.full(4 * power + 1, 1.0)
# P = add_polynomial_features(X, power)
# reg.fit_(P, Y, alpha = 3e-13, n_cycle = 100000)
# print("cost ", power, " : ", reg.cost_(reg.predict_(P), Y), "\n")
# power = 4
# reg.thetas = np.full(4 * power, 1)
# P = add_polynomial_features(X, power)
# reg.fit_(P, Y, alpha = 1e-17, n_cycle = 1000000)
# print("cost ", power, " : ", reg.cost_(reg.predict_(P), Y), "\n")
# power = 5
# reg.thetas = np.full(power * 3 + 1, 1.0)
import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error
from mylinearregression import MyLinearRegression as MyLR
# import matplotlib.font_manager
# flist = matplotlib.font_manager.get_fontconfig_fonts()
# names = [print(matplotlib.font_manager.FontProperties(fname=fname).get_name()) for fname in flist]
# print("fonts:", "\n".join(flist))
data = pd.read_csv("../../ressources/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([[89.0], [-8]]))
linear_model2 = MyLR(np.array([[89.0], [-6]]))
Y_model1 = linear_model1.predict_(Xpill)
Y_model2 = linear_model2.predict_(Xpill)
print(Xpill)
print(linear_model1.cost_(Y_model1, Yscore))
# 57.60304285714282
print(mean_squared_error(Yscore, Y_model1))
# 57.603042857142825
MyLR.plot(Xpill, Yscore, Y_model1)
linear_model1.plotcost(Xpill, Yscore)
print(linear_model2.cost_(Y_model2, Yscore))
# 232.16344285714285
print(mean_squared_error(Yscore, Y_model2))
# 232.16344285714285
MyLR.plot(Xpill, Yscore, Y_model2)
import numpy as np
from mylinearregression import MyLinearRegression as MyLR
X = np.array([[1., 1., 2., 3.], [5., 8., 13., 21.], [34., 55., 89., 144.]])
Y = np.array([[23.], [48.], [218.]])
theta = np.array([[1.], [1.], [1.], [1.], [1]])
print("X", X.shape)
print("Y", Y.shape)
print("theta", theta.shape)
# MyLR().
mylr = MyLR(theta)
mylr.predict_(X)
mylr.cost_elem_(X, Y)
mylr.cost_(X, Y)
mylr.fit_(X, Y, alpha=1.6e-4, n_cycle=200000)
mylr.theta
mylr.predict_(X)
mylr.cost_elem_(X, Y)
mylr.cost_(X, Y)
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)
"""
if X.size == 0 or self.theta.size == 0 or \
(X.size != 0 and X.shape[1] + 1 != self.theta.shape[0]):
return None
# on commence par rajouter une colonne x0 qui vaut 1
X_1 = np.insert(X, 0, [1.], axis=1)
return np.dot(X_1, self.theta)
if __name__ == '__main__':
import numpy as np
from mylinearregression import MyLinearRegression as MyLR
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))
# => array([[8.], [48.], [323.]])
print(mylr.cost_elem_(X, Y))
# => array([[37.5], [0.], [1837.5]])
print(mylr.cost_(X, Y))
# => 1875.0
mylr.fit_(X, Y, alpha=1.6e-4, n_cycle=200000)
print(mylr.theta)
# => array([[18.023..], [3.323..], [-0.711..], [1.605..], [-0.1113..]])
print(mylr.predict_(X))
# => array([[23.499..], [47.385..], [218.079...]])
print(mylr.cost_elem_(X, Y))
# => array([[0.041..], [0.062..], [0.001..]])
print(mylr.cost_(X, Y))
# => 0.1056..
#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, )
#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))
#print(mean_squared_error(Yscore, Y_model2))
mylr8 = MyLinearRegression([[88.85], [-9.0], [0], [0], [0], [0], [0], [0],
[0]])
mylr9 = MyLinearRegression([[88.85], [-9.0], [0], [0], [0], [0], [0], [0], [0],
[0]])
mylr2.fit_(x2, y_train)
mylr2.alpha = 0.00001
mylr2.fit_(x2, y_train)
mylr2.alpha = 0.00003
mylr2.fit_(x2, y_train)
mylr2.alpha = 0.0001
mylr2.fit_(x2, y_train)
mylr2.alpha = 0.0003
mylr2.fit_(x2, y_train)
y_n.append(mylr2.cost_(x2_test, y_test))
mylr3.fit_(x3, y_train)
mylr3.alpha = 0.00001
mylr3.fit_(x3, y_train)
mylr3.alpha = 0.00003
mylr3.fit_(x3, y_train)
mylr3.alpha = 0.0001
mylr3.fit_(x3, y_train)
mylr3.alpha = 0.0003
mylr3.fit_(x3, y_train)
mylr3.alpha = 0.001
mylr3.fit_(x3, y_train)
y_n.append(mylr3.cost_(x3_test, y_test))
mylr4.fit_(x4, y_train)