def print_result(self, centers): whs = self.whs centers = centers[np.argsort(centers.prod(1))] x, best = self.metric(whs, centers) bpr, aat = ( best > self.thresh).mean(), (x > self.thresh).mean() * self.n logger.info( 'thresh=%.2f: %.4f best possible recall, %.2f anchors past thr' % (self.thresh, bpr, aat)) logger.info( 'n=%g, img_size=%s, metric_all=%.3f/%.3f-mean/best, past_thresh=%.3f-mean: ' % (self.n, self.size, x.mean(), best.mean(), x[x > self.thresh].mean())) logger.info('%d anchor cluster result: [w, h]' % self.n) for w, h in centers: logger.info('[%d, %d]' % (round(w), round(h)))

这是一个函数的定义，用于输出anchor boxes聚类结果。函数参数有self和centers，其中self代表类的实例本身，centers是经过聚类算法得到的簇中心点坐标。函数首先对centers按照簇大小从小到大排序，然后根据阈值计算最佳召回率和超过阈值的anchor boxes个数。接着输出一些聚类结果指标，包括平均和最好的指标值以及超过阈值的anchor boxes的平均值。最后输出每个anchor box的宽度和高度。

相关问题

def __init__(self, n_inputs, n_rules, learning_rate=0.01): self.n = n_inputs self.m = n_rules self.lr = learning_rate # Initialize MF parameters using k-means clustering kmeans = KMeans(n_clusters=1) x0 = [i for i in np.zeros(self.n)] kmeans.fit([x0]) centroids = kmeans.cluster_centers_.flatten() sigmas = np.ones(self.m) * (kmeans.inertia_ / self.m) ** 0.5 self.params = { "mf_params": np.random.rand(self.n*self.m*2), "out_params": np.random.rand((self.n+1)*self.m,) }修正错误

根据您提供的代码，可以将其修正如下：

def __init__(self, n_inputs, n_rules, learning_rate=0.01):
    self.n = n_inputs
    self.m = n_rules
    self.lr = learning_rate

    # Initialize MF parameters using k-means clustering
    kmeans = KMeans(n_clusters=self.m)
    x0 = np.random.rand(100, self.n)  # 用于聚类的样本点
    kmeans.fit(x0)
    centroids = kmeans.cluster_centers_  # 获取聚类中心
    sigmas = np.ones(self.m) * (kmeans.inertia_ / self.m) ** 0.5  # 计算标准差

    self.params = {
        "mf_params": np.concatenate([centroids.flatten(), sigmas.flatten()]),
        "out_params": np.random.rand((self.n+1)*self.m,)
    }

在修正后的代码中，我们将k-means聚类的聚类数设置为`self.m`，并使用随机生成的样本点`x0`进行聚类。然后，我们通过`kmeans.cluster_centers_`获取每个聚类的中心点，并通过计算`sigmas`来确定高斯隶属度函数的标准差。最后，我们将所有的高斯隶属度函数参数连接起来，构建成一个包含所有参数的`self.params["mf_params"]`数组。

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：

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。