def test_MyLinearRegressing():
x = np.array([[12.4956442], [21.5007972], [
31.5527382], [48.9145838], [57.5088733]])
y = np.array([[37.4013816], [36.1473236], [
45.7655287], [46.6793434], [59.5585554]])
lr1 = MyLR([2, 0.7])
# Example 0.0:
print(lr1.predict_(x), end="\n\n")
# Output:
# array([[10.74695094],
# [17.05055804],
# [24.08691674],
# [36.24020866],
# [42.25621131]])
# Example 0.1:
print(lr1.cost_elem_(lr1.predict_(x), y), end="\n\n")
# Output:
# array([[77.72116511],
# [49.33699664],
# [72.38621816],
# [37.29223426],
# [78.28360514]])
# Example 0.2:
print(lr1.cost_(lr1.predict_(x), y), end="\n\n")
# Output:
# 315.0202193084312
# Example 1.0:
# lr2 = MyLR([0, 0])
lr2 = MyLR([1, 1], 5e-8, 1500000)
lr2.fit_(x, y)
print(lr2.thetas, end="\n\n")
# Output:
# array([[1.40709365],
# [1.1150909]])
# Example 1.1:
print(lr2.predict_(x), end="\n\n")
# Output:
# array([[15.3408728],
# [25.38243697],
# [36.59126492],
# [55.95130097],
# [65.53471499]])
# Example 1.2:
print(lr2.cost_elem_(lr2.predict_(x), y), end="\n\n")
# Output:
# array([[35.6749755],
# [4.14286023],
# [1.26440585],
# [29.30443042],
# [22.27765992]])
# Example 1.3:
print(lr2.cost_(lr2.predict_(x), y), end="\n\n")
def plot_cost(x, y):
"""Plot the data and prediction line from three non-empty numpy.ndarray.
Args:
x: has to be an numpy.ndarray, a vector of dimension m * 1.
y: has to be an numpy.ndarray, a vector of dimension m * 1.
theta: has to be an numpy.ndarray, a vector of dimension 2 * 1.
Returns:
Nothing.
Raises:
This function should not raise any Exceptions.
"""
# plt.plot(x, y, 'o')
# x = np.linspace(-15,5,100)
plt.ylim((10, 50))
plt.xlim((-13, -4.5))
ran = 15
upd = ran * 2 / 6
for t0 in np.arange(89 - ran, 89 + ran, upd):
cost_list = []
theta_list = []
for t1 in np.arange(-8 - 100, -8 + 100, 0.1):
lr = MyLR(thetas=[t0, t1], alpha=1e-3, max_iter=50000)
y_ = lr.predict(x)
mse_c = lr.cost_(y, y_) #[0][0]
cost_list.append(mse_c)
theta_list.append(t1)
# print(cost_list[-1])
label = "θ[0]=" + str(int(t0 * 10) / 10)
print(label, "done!")
plt.plot(theta_list, cost_list, label=label)
plt.xlabel("θ[1]")
plt.ylabel("MSE(θ[0], θ[1])")
plt.legend(loc='upper left')
plt.show()
def plot2graphs(x: np.ndarray, y: np.ndarray) -> None:
linear_model = MyLR(np.array([[89.0], [-8]]), max_iter=500)
flag = 3
if flag & 1:
linear_model.fit_(x, y)
y_hat = linear_model.predict_(x)
plot_regression(x, y, y_hat)
if flag & 2:
plot_cost(x, y)
def plot_cost(x: np.ndarray, y: np.ndarray) -> None:
plt.xlabel("$θ_1$")
plt.ylabel("cost function $J(θ_0, θ_1)$")
plt.grid()
linear_model = MyLR(np.array([[0], [0]]), max_iter=500)
thetas_0 = range(85, 95, 2)
for t0 in thetas_0:
linear_model.thetas[0][0] = t0
npoints = 100
y_cost = [0] * npoints
thetas1 = np.linspace(-15, -3.8, npoints)
for i, t1 in enumerate(thetas1):
linear_model.thetas[1][0] = t1
y_hat = linear_model.predict_(x)
y_cost[i] = linear_model.cost_(y, y_hat)
plt.plot(thetas1, y_cost, label="$J(θ_0=%d, θ_1)$" % t0)
plt.legend()
plt.show()
# **************************************************************************** #
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
from my_linear_regression import MyLinearRegression as MyLR
data = pd.read_csv("../subjects/day01/resources/are_blue_pills_magics.csv")
Xpill = np.array(data["Micrograms"]).reshape(-1,1)
Yscore = np.array(data["Score"]).reshape(-1,1)
thetas = np.array([1, 1])
#plt.plot(Xpill, Yscore, 'o')
mlr = MyLR(thetas, alpha=0.05, n_cycle=5000)
#th = mlr.fit_(Xpill, Yscore)
#print(th)
#plt.plot(Xpill, (th[1] * Xpill + th[0]), '-r')
#plt.show()
for j in range(80, 100, 5):
res = []
for i in range(-11, -7, 1):
mlr.thetas = np.array([j, i])
dummy, y_hat = mlr.predict_(Xpill)
res.append(mlr.mse_(Yscore, y_hat))
np.array(res)
plt.plot(np.arange(-11, -7), res)
plt.show()
import numpy as np
from my_linear_regression import MyLinearRegression as MyLR
x = np.array([12.4956442, 21.5007972, 31.5527382, 48.9145838, 57.5088733])
y = np.array([37.4013816, 36.1473236, 45.7655287, 46.6793434, 59.5585554])
lr1 = MyLR([2, 0.7])
# Example 0.0:
print("Example 0.0")
print(lr1.predict_(x))
# Output:
# array([[10.74695094],
# [17.05055804],
# [24.08691674],
# [36.24020866],
# [42.25621131]])
# Example 0.1:
print("\nExample 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("\nExample 0.2")
print(lr1.cost_(lr1.predict_(x), y))
from my_linear_regression import MyLinearRegression as MyLR
import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error
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)
linear_model1 = MyLR(np.array([89.0, -8]))
linear_model2 = MyLR(np.array([89.0, -6]))
linear_model1.fit_ulti(Xpill, Yscore)
#Y_model1 = linear_model1.predict_(Xpill)
#Y_model2 = linear_model2.predict_(Xpill)
#print(Y_model1)
#print(Y_model2)
#print(linear_model1.cost_(Xpill, Yscore))
theta = linear_model1.fit_(Xpill, Yscore)
#print(theta)
import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error
from my_linear_regression import MyLinearRegression as MyLR
data = pd.read_csv("../resources/are_blue_pills_magics.csv")
# print(data)
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]]))
# linear_model2.plot_costs(Xpill, Yscore)
linear_model2.plot_best_h(Xpill, Yscore)
# Y_model1 = linear_model1.predict_(Xpill)
# Y_model2 = linear_model2.predict_(Xpill)
# print(linear_model1.cost_(Yscore, Y_model1))
# 57.60304285714282
# print(mean_squared_error(Yscore, Y_model1))
# 57.603042857142825
# print(linear_model2.cost_(Yscore, Y_model2))
# 232.16344285714285
# print(mean_squared_error(Yscore, Y_model2))
# 232.16344285714285
import numpy as np
from my_linear_regression import MyLinearRegression as MyLR
x = np.array([[12.4956442], [21.5007972], [31.5527382], [48.9145838],
[57.5088733]])
y = np.array([[37.4013816], [36.1473236], [45.7655287], [46.6793434],
[59.5585554]])
lr1 = MyLR([2, 0.7])
# Example 0.0:
print(lr1.predict_(x))
# Output:
# array([ [10.74695094],
# [17.05055804],
# [24.08691674],
# [36.24020866],
# [42.25621131]])
# Example 0.1:
cost_elems = lr1.cost_elem_(lr1.predict_(x), y)
print(cost_elems)
# Output:
# array([ [77.72116511],
# [49.33699664],
# [72.38621816],
# [37.29223426],
# [78.28360514]])
# Example 0.2:
print(lr1.cost_(lr1.predict_(x), y))
def print_costfn(t0, y):
for i in np.linspace(t0 - 10, t0 + 50, 3000):
linear_model3 = MyLR(np.array([[-10], [i]]))
Y_model3 = linear_model3.predict_(Xpill)
plt.plot(linear_model3.thetas[1], linear_model3.cost_(y, Y_model3),
'gs')
from sklearn.metrics import mean_squared_error
from my_linear_regression import MyLinearRegression as MyLR
def print_costfn(t0, y):
for i in np.linspace(t0 - 10, t0 + 50, 3000):
linear_model3 = MyLR(np.array([[-10], [i]]))
Y_model3 = linear_model3.predict_(Xpill)
plt.plot(linear_model3.thetas[1], linear_model3.cost_(y, Y_model3),
'gs')
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)
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)
linear_model1_2 = MyLR(linear_model1.fit_(Xpill, Yscore))
Y_model1_2 = linear_model1_2.predict_(Xpill)
print(linear_model1.cost_(Yscore, Y_model1) * 2)
print(mean_squared_error(Yscore, Y_model1))
print(linear_model1.cost_(Yscore, Y_model2) * 2)
print(mean_squared_error(Yscore, Y_model2))
plt.plot(Xpill, Y_model1_2, 'gs')
plt.plot(Xpill, Y_model1_2, 'g--', label="Spredict(pills)")
plt.plot(Xpill, Yscore, 'bo', label="Strue")
import numpy as np
from my_linear_regression import MyLinearRegression as MyLR
if __name__ == "__main__":
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("# Example 0:")
print(mylr.predict(X))
print("# Output:")
print("array([[8.], [48.], [323.]])")
print()
print("# Example 1:")
print(mylr.cost_elem_(X,Y))
print("# Output:")
print("array([[37.5], [0.], [1837.5]])")
print()
print("# Example 2:")
print(mylr.cost_(X,Y))
print("# Output:")
print(1875.0)
print()
# sys.lol()
print("# Example 3:")
mylr.fit_(X, Y)
print(mylr.theta)
print("# Output:")
import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error
from my_linear_regression import MyLinearRegression as MyLR
import matplotlib.pyplot as plt
from polynomial_model import add_polynomial_features
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)
linear_model1 = MyLR(np.array([[89.0], [-8]]))
Y_model1 = linear_model1.predict(Xpill)
def continuous_plot(x, y, i, lr):
# Build the model:
# Plot:
## To get a smooth curve, we need a lot of data points
continuous_x = np.arange(1, 7.01, 0.01).reshape(-1, 1)
x_ = add_polynomial_features(continuous_x, i)
y_hat = lr.predict(x_)
print(x.shape, y.shape)
plt.scatter(x.T[0], y)
plt.plot(continuous_x, y_hat, color='orange')
plt.show()
cost = []
x = add_polynomial_features(Xpill, 10)
big_theta = [[2.03333758e-06], [4.76503382e-06], [1.29939248e-05],
# print("Ex 1.a")
# my_lreg = MyLR([1.0, 1.0], alpha=1e-3, max_iter=60000)
# my_lreg.fit_(X_1, Y)
# my_lreg.scatter(X_1, Y)
#
# print("Ex 1.b")
# my_lreg = MyLR([1.0, 1.0], alpha=1e-4, max_iter=60000)
# my_lreg.fit_(X_2, Y)
# my_lreg.scatter(X_2, Y)
#
# print("Ex 1.c")
# my_lreg = MyLR([1.0, 1.0], alpha=1e-4, max_iter=500000)
# my_lreg.fit_(X_3, Y)
# my_lreg.scatter(X_3, Y)
my_lreg = MyLR([1.0, 1.0, 1.0, 1.0], alpha=9e-5, max_iter=500000)
# print(my_lreg.mse_(X,Y).sum() / 2)
# print("144044.877...")
# print()
my_lreg.fit_(X, Y)
print(my_lreg.theta)
print("array([[334.994...],[-22.535...],[5.857...],[-2.586...]])")
print()
my_lreg.multi_scatter(X, Y)
# print(my_lreg.mse_(X,Y))
# print("586.896999...")
# print()
import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error
from my_linear_regression import MyLinearRegression as MyLR
import matplotlib.pyplot as plt
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)
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("Me: ", linear_model1.mse_(Yscore, Y_model1))
# print("Sc: ", mean_squared_error(Yscore, Y_model1))
# print()
#
# print("Me: ", linear_model2.mse_(Yscore, Y_model2))
# print("Sc: ", mean_squared_error(Yscore, Y_model2))
def plot(x, y, theta):
"""Plot the data and prediction line from three non-empty numpy.ndarray.
Args:
x: has to be an numpy.ndarray, a vector of dimension m * 1.
y: has to be an numpy.ndarray, a vector of dimension m * 1.
theta: has to be an numpy.ndarray, a vector of dimension 2 * 1.
Returns:
Nothing.
Raises:
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
from sklearn.metrics import mean_squared_error
from my_linear_regression import MyLinearRegression as MyLR
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([[89.0], [-8]]))
# linear_model2 = MyLR(np.array([[89.0], [-6]]))
Y_model1 = linear_model1.predict_(Xpill)
# Y_model2 = linear_model2.predict_(Xpill)
print(linear_model1.cost_(Xpill, Yscore)) # 57.60304285714282 >>>
print(mean_squared_error(Yscore, Y_model1)) # 57.603042857142825 >>>
# print(linear_model2.cost_(Xpill, Yscore)) # 232.16344285714285
# print(mean_squared_error(Yscore, Y_model2))
x= Xpill
y = Yscore
plt.scatter(x, y)
linear_model1.fit_(x, y)
plt.xlabel('Quantity of blue pill (in micrograms)')
plt.ylabel('Space driving score')
import numpy as np
from my_linear_regression import MyLinearRegression as MyLR
if __name__ == "__main__":
x = np.array([[12.4956442], [21.5007972], [31.5527382], [48.9145838],
[57.5088733]])
y = np.array([[37.4013816], [36.1473236], [45.7655287], [46.6793434],
[59.5585554]])
theta = np.array([2, 0.7])
x = x.reshape(len(x), 1)
y = y.reshape(len(y), 1)
theta = theta.reshape(len(theta), 1)
lr1 = MyLR(theta)
# Example 0.0:
print(lr1.predict_(x))
# Output:
"""
array([[10.74695094],
[17.05055804],
[24.08691674],
[36.24020866],
[42.25621131]])
"""
# Example 0.1:
print(lr1.cost_elem_(lr1.predict_(x), y))
# Output:
