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 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
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')
plt.title('simple plot')
plt.plot(x, linear_model1.predict_(x), color='green')
plt.legend(['S_true', 'S_predict'])
plt.show()
from my_linear_regression import MyLinearRegression
from polynomial_model import add_polynomial_features
if __name__ == "__main__":
x = np.arange(1, 11).reshape(-1, 1)
y = np.array([[1.39270298], [3.88237651], [4.37726357], [4.63389049],
[7.79814439], [6.41717461], [8.63429886], [8.19939795],
[10.37567392], [10.68238222]])
plt.scatter(x, y)
plt.show()
i = 2
arr = np.zeros(9)
l = list(range(2, 11))
while i <= 10:
x_ = add_polynomial_features(x, i)
my_lr = MyLinearRegression(np.ones(i + 1).reshape(-1, 1))
my_lr.fit_(x_, y)
arr[i - 2] = (my_lr.cost_(my_lr.predict_(x_), y))
continuous_x = np.arange(1, 10.01, 0.01).reshape(-1, 1)
x_ = add_polynomial_features(continuous_x, i)
y_hat = my_lr.predict_(x_)
plt.scatter(x, y)
plt.plot(continuous_x, y_hat, color='orange')
plt.show()
i += 1
plt.bar(l, arr, color='orange')
plt.show()
print(arr)
array2 = n[sep:, :]
return (array1[:, :-1], array2[:, :-1], array1[:, -1], array2[:, -1])
if __name__ == "__main__":
data = pd.read_csv("../resources/are_blue_pills_magics.csv")
x = np.array(data[['Micrograms']])
y = np.array(data[['Score']])
lst = data_spliter(x, y, 0.5)
x_train = lst[0]
y_train = lst[2]
y_train = y_train[:, np.newaxis]
x_test = lst[1]
y_test = lst[3]
y_test = y_test[:, np.newaxis]
i = 2
my_lr = MyLinearRegression([[1], [1]])
my_lr.fit_(x_train, y_train)
y_hat = my_lr.predict_(x_test)
print(my_lr.cost_(y_hat, y_test))
while i <= 10:
x_ = add_polynomial_features(x_train, i)
my_lr = MyLinearRegression(np.ones(i + 1).reshape(-1, 1))
my_lr.fit_(x_, y_train)
x_2 = add_polynomial_features(x_test, i)
y_hat = my_lr.predict_(x_2)
print(my_lr.cost_(y_hat, y_test))
i += 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) #lr7.fit_(x7, Y) #lr8.fit_(x8, Y) #lr9.fit_(x9, Y) y_1 = lr1.predict_(x1) y_2 = lr2.predict_(x2) y_3 = lr3.predict_(x3) y_4 = lr4.predict_(x4) #y_5 = lr5.predict_(x5) #y_6 = lr6.predict_(x6) #y_7 = lr7.predict_(x7) #y_8 = lr8.predict_(x8) #y_9 = lr9.predict_(x9) print(lr3.cost_(x3, Y)) plt.plot(X, Y, 'o') plt.plot(X, y_1, 'g') plt.plot(X, y_2, 'r') plt.plot(X, y_3, 'b') #plt.plot(X, y_4) #plt.plot(X, y_6) plt.show()
# [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))
# Output:
# 315.0202193084312
# Example 1.0:
lr2 = MyLR([1, 1], 5e-8, 1500000)
lr2.fit_(x, y)
print("\nExample 1.0")
print(lr2.thetas)
# Output:
# array([[1.40709365],
# [1.1150909 ]])
# Example 1.1:
print("\nExample 1.1")
print(lr2.predict_(x))
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')
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")
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()
for t0 in range(-10, -5, 1):
print_costfn(t0, Yscore)
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:")
print("array([[18.023..], [3.323..], [-0.711..], [1.605..], [-0.1113..]])")
print()
print("# Example 4:")
print(mylr.predict(X))
print("# Output:")