random.uniform(a,b)for i in x
时间: 2024-05-15 16:19:33 浏览: 12
This is not a valid statement in Python.
If you want to generate a list of random numbers between a and b using the uniform distribution, you can use the following code:
```
import random
x = [random.uniform(a,b) for i in range(n)]
```
Where `n` is the number of random numbers you want to generate.
相关问题
import random import numpy as np import matplotlib.pyplot as plt 生成随机坐标点 def generate_points(num_points): points = [] for i in range(num_points): x = random.uniform(-10, 10) y = random.uniform(-10, 10) points.append([x, y]) return points 计算欧几里得距离 def euclidean_distance(point1, point2): return np.sqrt(np.sum(np.square(np.array(point1) - np.array(point2)))) K-means算法实现 def kmeans(points, k, num_iterations=100): num_points = len(points) # 随机选择k个点作为初始聚类中心 centroids = random.sample(points, k) # 初始化聚类标签和距离 labels = np.zeros(num_points) distances = np.zeros((num_points, k)) for i in range(num_iterations): # 计算每个点到每个聚类中心的距离 for j in range(num_points): for l in range(k): distances[j][l] = euclidean_distance(points[j], centroids[l]) # 根据距离将点分配到最近的聚类中心 for j in range(num_points): labels[j] = np.argmin(distances[j]) # 更新聚类中心 for l in range(k): centroids[l] = np.mean([points[j] for j in range(num_points) if labels[j] == l], axis=0) return labels, centroids 生成坐标点 points = generate_points(100) 对点进行K-means聚类 k_values = [2, 3, 4] for k in k_values: labels, centroids = kmeans(points, k) # 绘制聚类结果 colors = [‘r’, ‘g’, ‘b’, ‘y’, ‘c’, ‘m’] for i in range(k): plt.scatter([points[j][0] for j in range(len(points)) if labels[j] == i], [points[j][1] for j in range(len(points)) if labels[j] == i], color=colors[i]) plt.scatter([centroid[0] for centroid in centroids], [centroid[1] for centroid in centroids], marker=‘x’, color=‘k’, s=100) plt.title(‘K-means clustering with k={}’.format(k)) plt.show()import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.cluster import KMeans from sklearn.datasets import load_iris 载入数据集 iris = load_iris() X = iris.data y = iris.target K-means聚类 kmeans = KMeans(n_clusters=3, random_state=0).fit(X) 可视化结果 plt.scatter(X[:, 0], X[:, 1], c=kmeans.labels_) plt.xlabel(‘Sepal length’) plt.ylabel(‘Sepal width’) plt.title(‘K-means clustering on iris dataset’) plt.show()对这个算法的结果用SSE,轮廓系数,方差比率准则,DBI几个指标分析
首先,我们需要定义这些指标:
1. SSE (Sum of Squared Errors):误差平方和,表示每个点到其所属簇中心的距离的平方和,用于评估聚类效果的好坏。
2. 轮廓系数 (Silhouette Coefficient):表示一个样本与其所属簇内其他样本的相似度,与其与最近簇的其他样本的相似度之间的比值,取值范围为[-1, 1],越接近1表示聚类效果越好。
3. 方差比率准则 (Variance Ratio Criterion):表示不同簇之间的距离与同一簇内部的距离的比值,用于评估聚类效果的好坏。
4. DBI (Davies-Bouldin Index):表示不同簇之间的距离与同一簇内部的距离之和的比值,用于评估聚类效果的好坏。
接下来,我们分别用这些指标来评估上面两段代码实现的K-means算法的聚类效果。
对于第一段代码,我们可以在K-means算法的函数中添加计算SSE的代码,并在函数返回值中返回SSE的值。同时,我们可以使用sklearn库中的metrics模块来计算轮廓系数。方差比率准则的计算与SSE类似,只需要将距离平方和改为距离的平方和,即可得到方差比率准则的值。DBI的计算可以使用sklearn库中的metrics模块中的davies_bouldin_score函数来实现。
代码如下所示:
```
import random import numpy as np import matplotlib.pyplot as plt from sklearn.metrics import silhouette_score from sklearn.metrics import davies_bouldin_score # 生成随机坐标点 def generate_points(num_points): points = [] for i in range(num_points): x = random.uniform(-10, 10) y = random.uniform(-10, 10) points.append([x, y]) return points # 计算欧几里得距离 def euclidean_distance(point1, point2): return np.sqrt(np.sum(np.square(np.array(point1) - np.array(point2)))) # K-means算法实现 def kmeans(points, k, num_iterations=100): num_points = len(points) # 随机选择k个点作为初始聚类中心 centroids = random.sample(points, k) # 初始化聚类标签和距离 labels = np.zeros(num_points) distances = np.zeros((num_points, k)) sse = 0 for i in range(num_iterations): # 计算每个点到每个聚类中心的距离 for j in range(num_points): for l in range(k): distances[j][l] = euclidean_distance(points[j], centroids[l]) # 根据距离将点分配到最近的聚类中心 for j in range(num_points): labels[j] = np.argmin(distances[j]) # 更新聚类中心 for l in range(k): centroids[l] = np.mean([points[j] for j in range(num_points) if labels[j] == l], axis=0) # 计算SSE sse = np.sum(np.square(distances[np.arange(num_points), labels])) # 计算轮廓系数 silhouette = silhouette_score(points, labels) # 计算方差比率准则 var_ratio = np.sum(np.min(distances, axis=1)) / sse # 计算DBI dbi = davies_bouldin_score(points, labels) return labels, centroids, sse, silhouette, var_ratio, dbi # 生成坐标点 points = generate_points(100) # 对点进行K-means聚类 k_values = [2, 3, 4] for k in k_values: labels, centroids, sse, silhouette, var_ratio, dbi = kmeans(points, k) # 绘制聚类结果 colors = ['r', 'g', 'b', 'y', 'c', 'm'] for i in range(k): plt.scatter([points[j][0] for j in range(len(points)) if labels[j] == i], [points[j][1] for j in range(len(points)) if labels[j] == i], color=colors[i]) plt.scatter([centroid[0] for centroid in centroids], [centroid[1] for centroid in centroids], marker='x', color='k', s=100) plt.title('K-means clustering with k={}'.format(k)) plt.show() print('SSE: {:.2f}'.format(sse)) print('Silhouette: {:.2f}'.format(silhouette)) print('Variance Ratio Criterion: {:.2f}'.format(var_ratio)) print('DBI: {:.2f}'.format(dbi))
```
对于第二段代码,我们可以使用sklearn库中的metrics模块来计算SSE、轮廓系数和DBI,方差比率准则的计算方法与第一段代码相同。
代码如下所示:
```
import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.cluster import KMeans from sklearn.datasets import load_iris from sklearn.metrics import silhouette_score from sklearn.metrics import davies_bouldin_score # 载入数据集 iris = load_iris() X = iris.data y = iris.target # K-means聚类 kmeans = KMeans(n_clusters=3, random_state=0).fit(X) # 计算SSE sse = np.sum(np.square(X - kmeans.cluster_centers_[kmeans.labels_])) # 计算轮廓系数 silhouette = silhouette_score(X, kmeans.labels_) # 计算方差比率准则 var_ratio = kmeans.inertia_ / sse # 计算DBI dbi = davies_bouldin_score(X, kmeans.labels_) # 可视化结果 plt.scatter(X[:, 0], X[:, 1], c=kmeans.labels_) plt.xlabel('Sepal length') plt.ylabel('Sepal width') plt.title('K-means clustering on iris dataset') plt.show() print('SSE: {:.2f}'.format(sse)) print('Silhouette: {:.2f}'.format(silhouette)) print('Variance Ratio Criterion: {:.2f}'.format(var_ratio)) print('DBI: {:.2f}'.format(dbi))
```
通过这些指标的计算,我们可以得到K-means算法的聚类效果的好坏。一般来说,SSE和轮廓系数越小,方差比率准则越大,DBI越小,则聚类效果越好。
class Givens(): def __init__(self,Tm,Tn,X): self.Tm=Tm self.Tn=Tn self.X1=X[0:10] self.X2=X[10:16] def hbf_T(self): Tm = self.Tm Tn = self.Tn a_b = np.random.uniform(0, 1, (Tm, Tn, 4)) c = a_b[:, :, 0]**2 + a_b[:, :, 1]**2 mask = c < 1 TT = np.zeros((Tm, Tn), dtype=complex) # 初始化 TT det_TT = 1 while det_TT != 0: for i in range(Tn): X1 = np.zeros(Tm, dtype=complex) X1[mask[:, i]] = a_b[:, i, 0][mask[:, i]] + 1j*a_b[:, i, 1][mask[:, i]] TT[:, i] = X1 det_TT = np.linalg.det(np.dot(np.transpose(TT), TT)) return TT
这段代码实现了一个 Givens 变换(Givens rotation)。Givens 变换是一种矩阵旋转,可以将一个矩阵的某两行或某两列通过正交变换旋转到一个新的位置,从而得到一个更简单的矩阵。这段代码中,Givens 变换作用于一个复矩阵,其中 X1 和 X2 是矩阵 X 的前 10 行和后 6 行,Tm 和 Tn 是 Givens 变换的参数。具体地,代码中通过随机生成一个 a_b 矩阵,然后选择其中符合条件的部分进行 Givens 变换,直到得到满足条件的 TT 矩阵。最后返回 TT 矩阵。
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