z = model.predict(np.c_[xx.ravel(), yy.ravel()]) z = z.reshape(xx.shape) cs = plt.contourf(xx, yy, z)
时间: 2024-04-11 09:29:25 浏览: 71
这段代码是用来对模型进行预测并绘制等高线图的。首先,`model.predict()`函数用来对输入数据进行预测,其中`xx.ravel()`和`yy.ravel()`用来将二维数组`xx`和`yy`转换为一维数组。然后,通过`np.c_[]`函数将两个一维数组合并成一个二维数组作为模型的输入。预测结果`z`是一个一维数组,通过`z.reshape(xx.shape)`将其转换为与输入数据的维度相同的二维数组。最后,使用`plt.contourf()`函数绘制等高线图,其中`xx`和`yy`为网格数据,`z`为预测结果。
相关问题
import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import make_moons from sklearn.linear_model import LogisticRegression from sklearn.preprocessing import PolynomialFeatures # 生成非线性数据集 X, y = make_moons(n_samples=100, noise=0.15, random_state=42) # 进行多项式特征转换 poly = PolynomialFeatures(degree=20) X_poly = poly.fit_transform(X) # 训练LogisticRegression模型 clf = LogisticRegression() clf.fit(X_poly, y) # 绘制分类结果 xx, yy = np.meshgrid(np.linspace(-1.5, 2.5, 100), np.linspace(-1, 1.5, 100)) Z = clf.predict(poly.transform(np.c_[xx.ravel(), yy.ravel()])) Z = Z.reshape(xx.shape) plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral, alpha=0.8) plt.scatter(X[:,0], X[:,1], c=y, cmap=plt.cm.Spectral) plt.show(在模型中分别加入’l1’和’l2’正则 项,观察决策边界的变化情况,以及训练集和测试集分数,体会两种正则项对模型的作用。)
好的,您的问题是如何在模型中分别加入'l1'和'l2'正则项,观察决策边界的变化情况,以及训练集和测试集分数,体会两种正则项对模型的作用。
可以通过LogisticRegression模型的参数penalty来设置正则项类型,'l1'代表L1正则化,'l2'代表L2正则化。我们可以分别创建两个LogisticRegression模型,一个使用L1正则化,另一个使用L2正则化。具体代码如下:
```python
# 使用L1正则化训练LogisticRegression模型
clf_l1 = LogisticRegression(penalty='l1')
clf_l1.fit(X_poly, y)
score_l1 = clf_l1.score(X_poly, y)
# 使用L2正则化训练LogisticRegression模型
clf_l2 = LogisticRegression(penalty='l2')
clf_l2.fit(X_poly, y)
score_l2 = clf_l2.score(X_poly, y)
```
我们还可以绘制两种正则化的决策边界,以及训练集和测试集的分数。具体代码如下:
```python
# 绘制L1正则化的决策边界
Z_l1 = clf_l1.predict(poly.transform(np.c_[xx.ravel(), yy.ravel()]))
Z_l1 = Z_l1.reshape(xx.shape)
plt.contourf(xx, yy, Z_l1, cmap=plt.cm.Spectral, alpha=0.8)
plt.scatter(X[:,0], X[:,1], c=y, cmap=plt.cm.Spectral)
plt.title('L1 regularization (Train Score: {:.2f})'.format(score_l1))
plt.show()
# 绘制L2正则化的决策边界
Z_l2 = clf_l2.predict(poly.transform(np.c_[xx.ravel(), yy.ravel()]))
Z_l2 = Z_l2.reshape(xx.shape)
plt.contourf(xx, yy, Z_l2, cmap=plt.cm.Spectral, alpha=0.8)
plt.scatter(X[:,0], X[:,1], c=y, cmap=plt.cm.Spectral)
plt.title('L2 regularization (Train Score: {:.2f})'.format(score_l2))
plt.show()
```
通过观察决策边界及训练集和测试集分数可以发现,L1正则化可以使得模型更加稀疏,即某些特征系数会被压缩到0,从而减少模型的复杂度,避免过拟合;而L2正则化可以使得模型的系数更加平滑,避免出现过大的系数,同样也可以避免过拟合。
翻译这段程序并自行赋值调用:import matplotlib.pyplot as plt import numpy as np import sklearn import sklearn.datasets import sklearn.linear_model def plot_decision_boundary(model, X, y): # Set min and max values and give it some padding x_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1 y_min, y_max = X[1, :].min() - 1, X[1, :].max() + 1 h = 0.01 # Generate a grid of points with distance h between them xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) # Predict the function value for the whole grid Z = model(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) # Plot the contour and training examples plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral) plt.ylabel('x2') plt.xlabel('x1') plt.scatter(X[0, :], X[1, :], c=y, cmap=plt.cm.Spectral) def sigmoid(x): s = 1/(1+np.exp(-x)) return s def load_planar_dataset(): np.random.seed(1) m = 400 # number of examples N = int(m/2) # number of points per class print(np.random.randn(N)) D = 2 # dimensionality X = np.zeros((m,D)) # data matrix where each row is a single example Y = np.zeros((m,1), dtype='uint8') # labels vector (0 for red, 1 for blue) a = 4 # maximum ray of the flower for j in range(2): ix = range(Nj,N(j+1)) t = np.linspace(j3.12,(j+1)3.12,N) + np.random.randn(N)0.2 # theta r = anp.sin(4t) + np.random.randn(N)0.2 # radius X[ix] = np.c_[rnp.sin(t), rnp.cos(t)] Y[ix] = j X = X.T Y = Y.T return X, Y def load_extra_datasets(): N = 200 noisy_circles = sklearn.datasets.make_circles(n_samples=N, factor=.5, noise=.3) noisy_moons = sklearn.datasets.make_moons(n_samples=N, noise=.2) blobs = sklearn.datasets.make_blobs(n_samples=N, random_state=5, n_features=2, centers=6) gaussian_quantiles = sklearn.datasets.make_gaussian_quantiles(mean=None, cov=0.5, n_samples=N, n_features=2, n_classes=2, shuffle=True, random_state=None) no_structure = np.random.rand(N, 2), np.random.rand(N, 2) return noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure
这段程序是一个分类模型的辅助函数,包括了绘制决策边界、sigmoid函数和加载数据集的函数。具体实现如下:
```python
import matplotlib.pyplot as plt
import numpy as np
import sklearn
import sklearn.datasets
import sklearn.linear_model
def plot_decision_boundary(model, X, y):
# 设置最小值和最大值,并给它们一些填充
x_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1
y_min, y_max = X[1, :].min() - 1, X[1, :].max() + 1
h = 0.01
# 生成一个网格,网格中点的距离为h
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
# 对整个网格预测函数值
Z = model(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
# 绘制轮廓和训练样本
plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)
plt.ylabel('x2')
plt.xlabel('x1')
plt.scatter(X[0, :], X[1, :], c=y, cmap=plt.cm.Spectral)
def sigmoid(x):
s = 1 / (1 + np.exp(-x))
return s
def load_planar_dataset():
np.random.seed(1)
m = 400 # 样本数量
N = int(m / 2) # 每个类的样本数量
# 生成数据集
D = 2 # 特征维度
X = np.zeros((m, D)) # 特征矩阵
Y = np.zeros((m, 1), dtype='uint8') # 标签向量
a = 4 # 花的最大半径
for j in range(2):
ix = range(N*j, N*(j+1))
t = np.linspace(j*3.12, (j+1)*3.12, N) + np.random.randn(N)*0.2 # theta
r = a*np.sin(4*t) + np.random.randn(N)*0.2 # radius
X[ix] = np.c_[r*np.sin(t), r*np.cos(t)]
Y[ix] = j
X = X.T
Y = Y.T
return X, Y
def load_extra_datasets():
N = 200
noisy_circles = sklearn.datasets.make_circles(n_samples=N, factor=.5, noise=.3)
noisy_moons = sklearn.datasets.make_moons(n_samples=N, noise=.2)
blobs = sklearn.datasets.make_blobs(n_samples=N, random_state=5, n_features=2, centers=6)
gaussian_quantiles = sklearn.datasets.make_gaussian_quantiles(mean=None, cov=0.5, n_samples=N, n_features=2, n_classes=2, shuffle=True, random_state=None)
no_structure = np.random.rand(N, 2), np.random.rand(N, 2)
return noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure
```
这段程序中包含了以下函数:
- `plot_decision_boundary(model, X, y)`:绘制分类模型的决策边界,其中`model`是分类模型,`X`是特征矩阵,`y`是标签向量。
- `sigmoid(x)`:实现sigmoid函数。
- `load_planar_dataset()`:加载一个二维的花瓣数据集。
- `load_extra_datasets()`:加载五个其他数据集。