import numpy as np
from my_linear_regression import My_linear_regression as MLR
def add_polynomial_features(x, power):
power = range(1, power + 1)
init = x
for pow in power:
x = np.column_stack((x, init**pow))
return x
data = pd.read_csv("../resources/spacecraft_data.csv")
X = np.array(data[['Age', 'Thrust_power', 'Terameters']])
Y = np.array(data["Sell_price"])
LR = MLR(np.ones((X.shape[1] + 1, 1)))
print(LR.cost_function(LR.predict(X), Y))
LR.fit(X, Y, alpha=1e-5, max_iter=5000000)
# print(LR.cost_function(LR.predict(X), Y))
# P = add_polynomial_features(X, 2)
# LR.theta = np.full(3 ** 2 + 1,1)
# LR.fit(P, Y, alpha = 1e-9, max_iter = 5000000)
# print(LR.cost_function(LR.predict(P), Y))
# P = add_polynomial_features(X, 3)
# LR.theta = np.full(10 + 3,1)
# LR.fit(P, Y, alpha = 1e-12, max_iter = 5000000)
# print(LR.cost_function(LR.predict(P), Y))
# P = add_polynomial_features(X, 4)
# LR.theta = np.full(10 + 6,1)
# LR.fit(P, Y, alpha = 1e-9, max_iter = 5000000)
data = pd.read_csv("are_blue_pills_magics.csv")
X = np.array(data[['Micrograms']])
Y = np.array(data[['Score']])
x1 = poly_feat(X, 2)
x2 = poly_feat(X, 3)
x3 = poly_feat(X, 4)
x4 = poly_feat(X, 5)
x5 = poly_feat(X, 6)
x6 = poly_feat(X, 7)
x7 = poly_feat(X, 8)
x8 = poly_feat(X, 9)
x9 = poly_feat(X, 10)
lr1 = MLR([80, 1, 1])
lr2 = MLR([80, 1, 1, 1])
lr3 = MLR([80, 1, 1, 1, 1])
lr4 = MLR([80, 1, 1, 1, 1, 1])
lr5 = MLR([80, 1, 1, 1, 1, 1, 1])
lr6 = MLR([1, 1, 1, 1, 1, 1, 1, 1])
lr7 = MLR([1, 1, 1, 1, 1, 1, 1, 1, 1])
lr8 = MLR([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
lr9 = MLR([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
lr1.fit_(x1, Y)
lr2.fit_(x2, Y)
lr3.fit_(x3, Y)
lr4.fit_(x4, Y)
#lr5.fit_(x5, Y)
#lr6.fit_(x6, Y)
#normalise new_train
for i in range(10):
new_train[:, i] = minmax(new_train[:, i])
#for plotting of polynomial curves - cotinuous data set over range of original data
#then add polynomial features and normalise
continuous_x = np.arange(1, 7.01, 0.01).reshape(-1, 1)
x_ = add_polynomial_features(continuous_x, 10)
for i in range(10):
x_[:, i] = minmax(x_[:, i])
thetas = np.ones(11).reshape(-1, 1)
cost_values = []
thetas_list = []
mlr = MLR(thetas, alpha=0.009, n_cycle=5000)
for degree in range(2, 11):
mlr.thetas = thetas[:degree + 1]
thetas_list.append(mlr.fit_(new_train[:, :degree], y_train))
cost_values.append(
mlr.mse_(y_train,
mlr.predict_(new_train[:, :degree])[1]))
i = 2
for elem in thetas_list:
mlr.thetas = elem
y_hat = mlr.predict_(x_[:, :i])[1]
plt.plot(continuous_x, y_hat, '--')
plt.title(str(degree))
plt.title(('degree = ' + str(i) + ' cost: ' + str(cost_values[i - 2])))
plt.plot(x_train, y_train, 'go')
x_test = np.array([5, 4.3, 2, 2, 5, 6, 3.5]).reshape(-1, 1) y_test = np.array([39, 52, 70, 58, 50, 32, 62]).reshape(-1, 1) plt.plot(x_test, y_test, 'o') new = add_polynomial_features(x, 10) thetas = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] cost_values = [] #for plotting of polynomial curves - cotinuous data set over range of original data continuous_x = np.arange(1, 7.01, 0.01).reshape(-1, 1) x_ = add_polynomial_features(continuous_x, 10) degree = 2 mlr = MLR(np.array(thetas[:degree + 1]), alpha=0.002, n_cycle=1500) mlr.fit_(new[:, :degree], y) cost_values.append(mlr.mse_(y, mlr.predict_(new[:, :degree])[1])) y_hat = mlr.predict_(x_[:, :degree])[1] plt.plot(continuous_x, y_hat, '--') plt.title(str(degree)) plt.show() degree = 3 mlr = MLR(np.array(thetas[:degree + 1]), alpha=0.00005, n_cycle=4000) mlr.fit_(new[:, :degree], y) cost_values.append(mlr.mse_(y, mlr.predict_(new[:, :degree])[1])) y_hat = mlr.predict_(x_[:, :degree])[1] plt.plot(x, y, 'o') plt.plot(continuous_x, y_hat, '--') plt.title(str(degree))
import pandas as pd
import numpy as np
from my_linear_regression import My_linear_regression as MLR
data = pd.read_csv("../resources/spacecraft_data.csv")
X = np.array(data[['Age', 'Thrust_power', 'Terameters']])
Y = np.array(data["Sell_price"])
LR = MLR(np.array([1.0, 1.0, 1.0, 1.0]))
print(LR.cost_function(LR.predict(X), Y))
LR.fit(X, Y, alpha=1e-6, max_iter=60000000)
print(LR.cost_function(LR.predict(X), Y))
# LR.plot(X, Y, LR_age.linear_regression_cost_function(LR_age.predict(X), Y))
thetas = np.array([1, 1])
plt.ylabel("price")
#mlr_age = MLR(thetas, alpha=0.01, n_cycle=4000)
#plot(mlr_age, age, y, "age")
#mlr_thrust = MLR(thetas, alpha=0.00001, n_cycle=30)
#plot(mlr_thrust, tp, y, "thrust power")
#mlr_tm = MLR(thetas, alpha=0.00022, n_cycle=76000)
#plot(mlr_tm, tm, y, "terameters")
thetas = np.array([1, 1, 1, 1])
mlr_multi = MLR(thetas, alpha=0.00009, n_cycle=100)
#mlr_multi.plot_cost_change(features, y)
th = mlr_multi.fit_(features, y)
print(th)
y_hat = th[0]
i = 1
while i < 4:
y_hat += th[i] * features[:, i - 1:i]
i += 1
plt.plot(age, y, "ob")
plt.xlabel("age")
plt.plot(age, y_hat, "o", markersize=2)
plt.show()
plt.plot(tp, y, "ob")
plt.xlabel("thrust power")
# +:+ +:+ +:+ #
# By: ecross <*****@*****.**> +#+ +:+ +#+ #
# +#+#+#+#+#+ +#+ #
# Created: 2020/05/04 12:33:11 by ecross #+# #+# #
# Updated: 2020/05/04 12:46:15 by ecross ### ########.fr #
# #
# **************************************************************************** #
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_regression
from my_linear_regression import MyLinearRegression as MLR
if __name__ == "__main__":
x, y = make_regression(n_samples=100, n_features=1, noise=10)
theta = np.array([1, 1])
#mlr = MLR(theta, 0.001, 500)
#theta1 = mlr.fit_(x, y)
#plt.plot(x, y, 'o')
#plt.plot(x, (theta1[1] * x + theta1[0]), '-r')
mlr = MLR(theta, 0.5, 1100)
theta1 = mlr.fit_(x, y)
plt.plot(x, y, 'o')
plt.plot(x, (theta1[1] * x + theta1[0]), '-g')
plt.show()
for i in range(3):
plt.plot(x_train[:, i:i + 1], y_train, 'go')
plt.title(data.columns[i])
plt.plot(x_test[:, i:i + 1], y_test, 'ro', markersize=3)
plt.show()
#initialise thetas as array with feature number + 1 zeros
thetas = np.zeros(new_features.shape[1] + 1)
#should be able to use same alpha and cycle number for all, as same data
#carry out linear regression on training data
cost_list = []
mlr = MLR(thetas, alpha=0.1, n_cycle=400)
mlr.fit_(x_train, y_train)
y_hat = mlr.predict_(x_test)[1]
cost_list.append(mlr.mse_(y_test, y_hat))
plot(x_test, y_test, y_hat, features)
#carry out 9 ridge regressions on training data, with lambda from 0.1 to 0.9
mrg = MRG(thetas, alpha=0.1, n_cycle=400)
for i in range(1, 10):
mrg.lambda_ = i / 10
mrg.thetas = thetas
plt.title('lambda = ' + str(i / 10))
mrg.fit_(x_train, y_train)
y_hat = mrg.predict_(x_test)[1]
cost_list.append(mlr.mse_(y_test, y_hat))