各种机器学习算法分类器，线性回归——Python代码（KNN,MLP,SVM,随机森林，RF,GBDT）

参考链接：https://www.cnblogs.com/rubbninja/p/6186847.html

KNN 回归`

knn回归

from sklearn import neighbors
knn_reg = neighbors.KNeighborsRegressor(40, weights=‘uniform’, metric=‘euclidean’)
%time knn_reg.fit(offline_rss, offline_location)
%time predictions = knn_reg.predict(rss)
acc = accuracy(predictions, trace)
print "accuracy: ", acc/100, “m”

Logistic regression （逻辑斯蒂回归）

逻辑斯蒂回归是用来分类的

labels = np.round(offline_location[:, 0]/100.0) * 100 + np.round(offline_location[:, 1]/100.0)
from sklearn.linear_model import LogisticRegressionCV
clf_l2_LR_cv = LogisticRegressionCV(Cs=20, penalty=‘l2’, tol=0.001)
predict_labels = clf_l2_LR.fit(offline_rss, labels).predict(rss)
x = np.floor(predict_labels/100.0)
y = predict_labels - x * 100
predictions = np.column_stack((x, y)) * 100
acc = accuracy(predictions, trace)
print "accuracy: ", acc/100, ‘m’

Support Vector Machine for Regression （支持向量机）

from sklearn import svm
clf_x = svm.SVR(C=1000, gamma=0.01)
clf_y = svm.SVR(C=1000, gamma=0.01)
%time clf_x.fit(offline_rss, offline_location[:, 0])
%time clf_y.fit(offline_rss, offline_location[:, 1])
%time x = clf_x.predict(rss)
%time y = clf_y.predict(rss)
predictions = np.column_stack((x, y))
acc = accuracy(predictions, trace)
print "accuracy: ", acc/100, “m”

Support Vector Machine for Classification （支持向量机

from sklearn import svm
labels = np.round(offline_location[:, 0]/100.0) * 100 + np.round(offline_location[:, 1]/100.0)
clf_svc = svm.SVC(C=1000, tol=0.01, gamma=0.001)
%time clf_svc.fit(offline_rss, labels)
%time predict_labels = clf_svc.predict(rss)
x = np.floor(predict_labels/100.0)
y = predict_labels - x * 100
predictions = np.column_stack((x, y)) * 100
acc = accuracy(predictions, trace)
print "accuracy: ", acc/100, ‘m’

random forest regressor （随机森林）

from sklearn.ensemble import RandomForestRegressor
estimator = RandomForestRegressor(n_estimators=150)
%time estimator.fit(offline_rss, offline_location)
%time predictions = estimator.predict(rss)
acc = accuracy(predictions, trace)
print "accuracy: ", acc/100, ‘m’

random forest classifier （随机森林

from sklearn.ensemble import RandomForestClassifier
labels = np.round(offline_location[:, 0]/100.0) * 100 + np.round(offline_location[:, 1]/100.0)
estimator = RandomForestClassifier(n_estimators=20, max_features=None, max_depth=20) # 内存受限，tree的数量有点少
%time estimator.fit(offline_rss, labels)
%time predict_labels = estimator.predict(rss)
x = np.floor(predict_labels/100.0)
y = predict_labels - x * 100
predictions = np.column_stack((x, y)) * 100
acc = accuracy(predictions, trace)
print "accuracy: ", acc/100, ‘m’

Linear Regression （线性回归）

from sklearn.linear_model import LinearRegression
predictions = LinearRegression().fit(offline_rss, offline_location).predict(rss)
acc = accuracy(predictions, trace)
print "accuracy: ", acc/100, ‘m’

Ridge Regression （岭回归）

from sklearn.linear_model import RidgeCV
clf = RidgeCV(alphas=np.logspace(-4, 4, 10))
predictions = clf.fit(offline_rss, offline_location).predict(rss)
acc = accuracy(predictions, trace)
print "accuracy: ", acc/100, ‘m’

Lasso回归

from sklearn.linear_model import MultiTaskLassoCV
clf = MultiTaskLassoCV(alphas=np.logspace(-4, 4, 10))
predictions = clf.fit(offline_rss, offline_location).predict(rss)
acc = accuracy(predictions, trace)
print "accuracy: ", acc/100, ‘m’

Elastic Net （弹性网回归）

from sklearn.linear_model import MultiTaskElasticNetCV
clf = MultiTaskElasticNetCV(alphas=np.logspace(-4, 4, 10))
predictions = clf.fit(offline_rss, offline_location).predict(rss)
acc = accuracy(predictions, trace)
print "accuracy: ", acc/100, ‘m’

Bayesian Ridge Regression （贝叶斯岭回归）

from sklearn.linear_model import BayesianRidge
from sklearn.multioutput import MultiOutputRegressor
clf = MultiOutputRegressor(BayesianRidge())
predictions = clf.fit(offline_rss, offline_location).predict(rss)
acc = accuracy(predictions, trace)
print "accuracy: ", acc/100, “m”

Gradient Boosting for regression （梯度提升）

from sklearn import ensemble
from sklearn.multioutput import MultiOutputRegressor
clf = MultiOutputRegressor(ensemble.GradientBoostingRegressor(n_estimators=100, max_depth=10))
%time clf.fit(offline_rss, offline_location)
%time predictions = clf.predict(rss)
acc = accuracy(predictions, trace)
print "accuracy: ", acc/100, “m”

Multi-layer Perceptron regressor （神经网络多层感知器）

from sklearn.neural_network import MLPRegressor
clf = MLPRegressor(hidden_layer_sizes=(100, 100))
%time clf.fit(offline_rss, offline_location)
%time predictions = clf.predict(rss)
acc = accuracy(predictions, trace)
print "accuracy: ", acc/100, “m”