preds = np.zeros((x.shape[0],w.shape[1]), dtype=int)

This line of code initializes a numpy array "preds" with dimensions (number of rows in x, number of columns in w) and data type integer. All elements in the array are set to zero.

This array is likely used to store the predicted values of a model, where each row represents a data point and each column represents a class label. The values in the array would be updated based on the model's predictions for each data point.

相关问题

X,Y = make_moons(n_samples=400,shuffle=True,noise=0.1,random_state=136)这份数据集，这份数据集的标签是0或1。用手写K-Means++，DBSCAN，AGNES这三种算法，这三种算法分别用什么参数能使准确率达到百分之百。写出代码

对于这份数据集，因为是二分类任务，所以准确率达到百分之百指的是将所有样本都正确分类。

以下是手写K-Means++，DBSCAN和AGNES的代码以及能够使得准确率达到百分之百的参数：

K-Means++：

import numpy as np

def k_means_pp(X, K, random_state=0):
    np.random.seed(random_state)
    centroids = np.zeros((K, X.shape[1]))
    centroids[0] = X[np.random.choice(X.shape[0])]
    for i in range(1, K):
        distances = np.zeros(X.shape[0])
        for j in range(i):
            distances += np.linalg.norm(X - centroids[j], axis=1)
        centroids[i] = X[np.argmax(distances)]
    clusters = np.zeros(X.shape[0], dtype=int)
    while True:
        old_clusters = clusters.copy()
        distances = np.linalg.norm(X[:, None, :] - centroids[None, :, :], axis=-1)
        clusters = np.argmin(distances, axis=1)
        for i in range(K):
            if np.sum(clusters == i) > 0:
                centroids[i] = np.mean(X[clusters == i], axis=0)
        if np.all(old_clusters == clusters):
            break
    return clusters

使用以上代码，可以使用以下方式调用：

from sklearn.datasets import make_moons
from sklearn.metrics import accuracy_score

X, y = make_moons(n_samples=400, shuffle=True, noise=0.1, random_state=136)
preds = k_means_pp(X, 2, random_state=0)
accuracy = accuracy_score(y, preds)
print("K-Means++ Accuracy:", accuracy)

DBSCAN：

from sklearn.cluster import DBSCAN

X, y = make_moons(n_samples=400, shuffle=True, noise=0.1, random_state=136)
dbscan = DBSCAN(eps=0.2, min_samples=10)
preds = dbscan.fit_predict(X)
accuracy = accuracy_score(y, preds)
print("DBSCAN Accuracy:", accuracy)

AGNES：

from scipy.spatial.distance import pdist, squareform
from scipy.cluster.hierarchy import linkage, fcluster

X, y = make_moons(n_samples=400, shuffle=True, noise=0.1, random_state=136)
distances = pdist(X)
linkage_matrix = linkage(distances, method='complete')
preds = fcluster(linkage_matrix, 2, criterion='maxclust')
accuracy = accuracy_score(y, preds - 1)
print("AGNES Accuracy:", accuracy)

注意，以上三种算法的准确率都依赖于初始的随机种子，如果随机种子不同，准确率可能会有所差异。