机器学习算法实战案例

1、svm算法

from sklearn.svm import SVC

svm = SVC(kernel='rbf', random_state=0, gamma=0.2, C=1.0)

svm.fit(featureListArray)

pred = svm.predict(featureListArray)

print(pred)

2、KMeans算法

>>> from sklearn.cluster import KMeans

>>> import numpy as np

>>> X = np.array([[1, 2], [1, 4], [1, 0],

... [4, 2], [4, 4], [4, 0]])

>>> kmeans = KMeans(n_clusters=2, random_state=0).fit(X)

>>> kmeans.labels_

array([0, 0, 0, 1, 1, 1], dtype=int32)

>>> kmeans.predict([[0, 0], [4, 4]])

array([0, 1], dtype=int32)

>>> kmeans.cluster_centers_

array([[1., 2.],

[4., 2.]])

fit(X[, y, sample_weight])Compute k-means clustering.fit_predict(X[, y, sample_weight])Compute cluster centers and predict cluster index for each sample.fit_transform(X[, y, sample_weight])Compute clustering and transform X to cluster-distance space.get_params([deep])Get parameters for this estimator.predict(X[, sample_weight])Predict the closest cluster each sample in X belongs to.score(X[, y, sample_weight])Opposite of the value of X on the K-means objective.set_params(**params)Set the parameters of this estimator.transform(X)Transform X to a cluster-distance space.

3、GMM聚类

from sklearn import mixture

clf = mixture.GaussianMixture(n_components=2, covariance_type='full')

clf.fit(featureListArray)

pred = clf.predict(featureListArray)

print(pred)

4、One-svm

# # TODO: 异常检测 = one-svm / SVDD /...

# print()

# print("异常检测...")

# print("model training...")

# # 定义 OneClassSvm

# clf = svm.OneClassSVM(nu=0.5, kernel="rbf", gamma=0.1)

# # OneClassSVM 模型训练

# clf.fit(newFeaturenArray)

#

# # # 保存 One-SVM 模型

# if not os.path.exists('model/svm'):

# os.makedirs('model/svm')

# print("model saving...")

# joblib.dump(clf, 'model/svm/svm_clf.model')

网址：机器学习算法实战案例 https://www.yuejiaxmz.com/news/view/64007

相关内容

博弈论+机器学习=？
机器学习（七）：提升（boosting）方法
职场生存法则：策略与实战案例分析
算法在身边——学习算法从妈妈的菜谱开始
计算机学习心得体会（精选9篇）
计算机组装与维护
计算机维护实习报告（46篇）
计算机硬件日常维护范例6篇
学生心理辅导案例范文
司法部发布贯彻实施法律援助法典型案例

随便看看