# Python回归模型、梯度下降及多项式回归模型源代码

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• 2022-05-26 20:35
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Python回归模型、梯度下降及多项式回归模型源代码
training_linear_models.rar
• training_linear_models.py
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#!/usr/bin/env python # coding: utf-8 # *– Training Models** # # <table align="left"> # <td> # </table> # # Setup # First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20. # In[1]: # Python ≥3.5 is required import sys assert sys.version_info >= (3, 5) # Scikit-Learn ≥0.20 is required import sklearn assert sklearn.__version__ >= "0.20" # Common imports import numpy as np import os # to make this notebook's output stable across runs np.random.seed(42) # To plot pretty figures from IPython import get_ipython #get_ipython().run_line_magic('matplotlib', 'inline') #get_ipython().run_line_magic('matplotlib', 'auto') import matplotlib as mpl import matplotlib.pyplot as plt mpl.rc('axes', labelsize=14) mpl.rc('xtick', labelsize=12) mpl.rc('ytick', labelsize=12) # Where to save the figures PROJECT_ROOT_DIR = "." CHAPTER_ID = "training_linear_models" IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, "images", CHAPTER_ID) os.makedirs(IMAGES_PATH, exist_ok=True) def save_fig(fig_id, tight_layout=True, fig_extension="png", resolution=300): path = os.path.join(IMAGES_PATH, fig_id + "." + fig_extension) print("Saving figure", fig_id) if tight_layout: plt.tight_layout() plt.savefig(path, format=fig_extension, dpi=resolution) # # Linear Regression # ## The Normal Equation # In[2]: import numpy as np X = 2 * np.random.rand(100, 1) y = 4 + 3 * X + np.random.randn(100, 1) # In[3]: plt.plot(X, y, "b.") plt.xlabel("$x_1$", fontsize=18) plt.ylabel("$y$", rotation=0, fontsize=18) plt.axis([0, 2, 0, 15]) save_fig("generated_data_plot") plt.show() # In[4]: X_b = np.c_[np.ones((100, 1)), X] # add x0 = 1 to each instance theta_best = np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(y) # In[5]: theta_best # In[6]: X_new = np.array([[0], [2]]) X_new_b = np.c_[np.ones((2, 1)), X_new] # add x0 = 1 to each instance y_predict = X_new_b.dot(theta_best) y_predict # In[7]: plt.plot(X_new, y_predict, "r-") plt.plot(X, y, "b.") plt.axis([0, 2, 0, 15]) plt.show() # The figure in the book actually corresponds to the following code, with a legend and axis labels: # In[8]: plt.plot(X_new, y_predict, "r-", linewidth=2, label="Predictions") plt.plot(X, y, "b.") plt.xlabel("$x_1$", fontsize=18) plt.ylabel("$y$", rotation=0, fontsize=18) plt.legend(loc="upper left", fontsize=14) plt.axis([0, 2, 0, 15]) save_fig("linear_model_predictions_plot") plt.show() # In[9]: from sklearn.linear_model import LinearRegression lin_reg = LinearRegression() lin_reg.fit(X, y) lin_reg.intercept_, lin_reg.coef_ # In[10]: lin_reg.predict(X_new) # The LinearRegression class is based on the scipy.linalg.lstsq() function (the name stands for "least squares"), which you could call directly: # In[11]: theta_best_svd, residuals, rank, s = np.linalg.lstsq(X_b, y, rcond=1e-6) theta_best_svd # This function computes $\mathbf{X}^+\mathbf{y}$, where $\mathbf{X}^{+}$ is the _pseudoinverse_ of $\mathbf{X}$ (specifically the Moore-Penrose inverse). You can use np.linalg.pinv() to compute the pseudoinverse directly: # In[12]: np.linalg.pinv(X_b).dot(y) # # Gradient Descent # ## Batch Gradient Descent # In[13]: eta = 0.1 # learning rate n_iterations = 1000 m = 100 theta = np.random.randn(2,1) # random initialization for iteration in range(n_iterations): gradients = 2/m * X_b.T.dot(X_b.dot(theta) - y) theta = theta - eta * gradients # In[14]: theta # In[15]: X_new_b.dot(theta) # In[16]: theta_path_bgd = [] def plot_gradient_descent(theta, eta, theta_path=None): m = len(X_b) plt.plot(X, y, "b.") n_iterations = 1000 for iteration in range(n_iterations): if iteration < 10: y_predict = X_new_b.dot(theta) style = "b-" if iteration > 0 else "r--" plt.plot(X_new, y_predict, style) gradients = 2/m * X_b.T.dot(X_b.dot(theta) - y) theta = theta - eta * gradients if theta_path is not None: theta_path.append(theta) plt.xlabel("$x_1$", fontsize=18) plt.axis([0, 2, 0, 15]) plt.title(r"$\eta = {}$".format(eta), fontsize=16) # In[17]: np.random.seed(42) theta = np.random.randn(2,1) # random initialization plt.figure(figsize=(10,4)) plt.subplot(131); plot_gradient_descent(theta, eta=0.02) plt.ylabel("$y$", rotation=0, fontsize=18) plt.subplot(132); plot_gradient_descent(theta, eta=0.1, theta_path=theta_path_bgd) plt.subplot(133); plot_gradient_descent(theta, eta=0.5) save_fig("gradient_descent_plot") plt.show() # ## Stochastic Gradient Descent # In[18]: theta_path_sgd = [] m = len(X_b) np.random.seed(42) # In[19]: n_epochs = 50 t0, t1 = 5, 50 # learning schedule hyperparameters def learning_schedule(t): return t0 / (t + t1) theta = np.random.randn(2,1) # random initialization for epoch in range(n_epochs): for i in range(m): if epoch == 0 and i < 20: # not shown in the book y_predict = X_new_b.dot(theta) # not shown style = "b-" if i > 0 else "r--" # not shown plt.plot(X_new, y_predict, style) # not shown random_index = np.random.randint(m) xi = X_b[random_index:random_index+1] yi = y[random_index:random_index+1] gradients = 2 * xi.T.dot(xi.dot(theta) - yi) eta = learning_schedule(epoch * m + i) theta = theta - eta * gradients theta_path_sgd.append(theta) # not shown plt.plot(X, y, "b.") # not shown plt.xlabel("$x_1$", fontsize=18) # not shown plt.ylabel("$y$", rotation=0, fontsize=18) # not shown plt.axis([0, 2, 0, 15]) # not shown save_fig("sgd_plot") # not shown plt.show() # not shown # In[20]: theta # In[21]: from sklearn.linear_model import SGDRegressor sgd_reg = SGDRegressor(max_iter=1000, tol=1e-3, penalty=None, eta0=0.1, random_state=42) sgd_reg.fit(X, y.ravel()) # In[22]: sgd_reg.intercept_, sgd_reg.coef_ # ## Mini-batch gradient descent # In[23]: theta_path_mgd = [] n_iterations = 50 minibatch_size = 20 np.random.seed(42) theta = np.random.randn(2,1) # random initialization t0, t1 = 200, 1000 def learning_schedule(t): return t0 / (t + t1) t = 0 for epoch in range(n_iterations): shuffled_indices = np.random.permutation(m) X_b_shuffled = X_b[shuffled_indices] y_shuffled = y[shuffled_indices] for i in range(0, m, minibatch_size): t += 1 xi = X_b_shuffled[i:i+minibatch_size] yi = y_shuffled[i:i+minibatch_size] gradients = 2/minibatch_size * xi.T.dot(xi.dot(theta) - yi) eta = learning_schedule(t) theta = theta - eta * gradients theta_path_mgd.append(theta) # In[24]: theta # In[25]: theta_path_bgd = np.array(theta_path_bgd) theta_path_sgd = np.array(theta_path_sgd) theta_path_mgd = np.array(theta_path_mgd) # In[26]: plt.figure(figsize=(7,4)) plt.plot(theta_path_sgd[:, 0], theta_path_sgd[:, 1], "r-s", linewidth=1, label="Stochastic") plt.plot(theta_path_mgd[:, 0], theta_path_mgd[:, 1], "g-+", linewidth=2, label="Mini-batch") plt.plot(theta_path_bgd[:, 0], theta_path_bgd[:, 1], "b-o", linewidth=3, label="Batch") plt.legend(loc="upper left", fontsize=16) plt.xlabel(r"$\theta_0$", fontsize=20) plt.ylabel(r"$\theta_1$ ", fontsize=20, rotation=0) plt.axis([2.5, 4.5, 2.3, 3.9]) save_fig("gradient_descent_paths_plot") plt.show() # # Polynomial Regression # In[27]: import numpy as np import numpy.random as rnd np.random.seed(42) # In[28]: m = 100 X = 6 * np.random.rand(m, 1) - 3 y = 0.5 * X**2 + X + 2 + np.

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