#导入库fromsklearn.datasetsimportmake_classificationimportmatplotlib.pyplotasplt#创建具有一个信息特征和一个每类簇的分类数据集X,y=make_classification(n_features=2,n_redundant=0,n_informative=1,n_clusters_per_class=1)#绘制数据集plt
from sklearn.ensemble import AdaBoostClassifier from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split # 生成模拟数据 X, y = make_classification(n_samples=1000, n_features=20, n_informative=2, n_redundant=10, random_state=42) # 划分训练集和测试集...
from sklearn.datasets import make_classification from sklearn.cluster import KMeans from matplotlib import pyplot # 定义数据集 X, _ = make_classification(n_samples=1000, n_features=2, n_informative=2, n_redundant=0, n_clusters_per_class=1, random_state=4) # 定义模型 model = KMeans(n_c...
y = make_classification(n_samples=2500, n_features=5,n_redundant=0, n_informative=5,n_classes=2, class_sep=0.3)# Test Implemented SVMsvm = SVM(kernel='rbf', k=1)svm.fit(X, y, eval_train=True)y_pred, _ = svm.predict(X)print(f"Accuracy:...
X, y = make_classification(n_samples=1000, n_features=20, n_informative=2, n_redundant=10, random_state=42) # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) ...
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一、聚类方法理论 二、10个聚类方法的汇总 #原始版本 # k-means 聚类 import numpy as np from numpy import where from sklearn.datasets import make_classification import sklearn.cl
model_selection import train_test_split # 创建一个包含一个信息特征和每个类别一个聚类的分类数据集 nb_samples = 300 X, Y = make_classification(n_samples=nb_samples, n_features=2, n_informative=2, n_redundant=0) X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_...
X, _ = make_classification(n_samples=1000, n_features=2, n_informative=2, n_redundant=0, n_clusters_per_class=1, random_state=4) # 定义模型 model = AffinityPropagation(damping=0.9) # 匹配模型 model.fit(X) # 为每个示例分配一个集群 ...
random.seed(1) X, y = make_classification(n_samples=2500, n_features=5, n_redundant=0, n_informative=5, n_classes=2, class_sep=0.3) # Test Implemented SVM svm = SVM(kernel='rbf', k=1) svm.fit(X, y, eval_train=True) y_pred, _ = svm.predict(X) print(f"Accuracy: {np....