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")
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)
"""
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..
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])
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:
# 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:
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)