def fit(self, x, y):
x_pow = []
xx = x.reshape(len(x), 1)
for i in range(1, self.degree + 1):
x_pow.append(xx**i)
mat = np.concatenate(x_pow, axis=1)
linreg = linearreg.LinerRegression()
linreg.fit(mat, y)
self.w_ = linreg.w_
from mpl_toolkits.mplot3d import axes3d
from numpy import linspace
import linearreg
if __name__ == "__main__":
x = np.arange(12)
y = 1 + 2 * x
y[2] = 20
y[4] = 0
xmin, xmax, ymin, ymax = 0, 12, -1, 25
flg, axes = plt.subplots(nrows=2, ncols=5)
for i in range(5):
xx = x[:2 * (i + 1)]
yy = y[:2 * (i + 1)]
model = linearreg.LinerRegression()
model.fit(xx, yy)
xs = [xmin, xmax]
ys = [
model.w_[0] + model.w_[1] * xmin, model.w_[0] + model.w_[1] * xmax
]
axes[0, i].set_xlim([xmin, xmax])
axes[0, i].set_ylim([ymin, ymax])
axes[0, i].scatter(xx, yy, color="k")
axes[0, i].plot(xs, ys, color="k")
model = ridge.RidgeRegression(10.)
model.fit(xx, yy)
xs = [xmin, xmax]
ys = [
model.w_[0] + model.w_[1] * xmin, model.w_[0] + model.w_[1] * xmax
if __name__ == "__main__":
xx = np.arange(0, 5, 0.01)
np.random.seed(0)
y_poly_sum = np.zeros(len(xx))
y_poly_sum_sq = np.zeros(len(xx))
y_lin_sum = np.zeros(len(xx))
y_lin_sum_sq = np.zeros(len(xx))
y_true = f(xx)
n = 100000
warnings.filterwarnings("ignore")
for _ in range(n):
x, y = sample(5)
poly = polyreg.PolynomialRegression(4)
poly.fit(x, y)
lin = linearreg.LinerRegression()
lin.fit(x, y)
y_poly = poly.predict(xx)
y_poly_sum += y_poly
y_poly_sum_sq += (y_poly - y_true)**2
y_lin = lin.predict(xx.reshape(-1, 1))
y_lin_sum += y_lin
y_lin_sum_sq += (y_lin - y_true)**2
flg = plt.figure()
ax1 = flg.add_subplot(121)
ax2 = flg.add_subplot(122)
ax1.set_title("Liner")
ax2.set_title("Polynomial")
ax1.set_ylim(0, 1)