本文整理汇总了Python中LogisticRegression.LogisticRegression.fit方法的典型用法代码示例。如果您正苦于以下问题：Python LogisticRegression.fit方法的具体用法？Python LogisticRegression.fit怎么用？Python LogisticRegression.fit使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类LogisticRegression.LogisticRegression的用法示例。
在下文中一共展示了LogisticRegression.fit方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: cross_validation
# 需要导入模块: from LogisticRegression import LogisticRegression [as 别名]
# 或者: from LogisticRegression.LogisticRegression import fit [as 别名]
def cross_validation(X,y,bsize, fold, eta, solver="SGD", wdecay=0):
from sklearn.cross_validation import StratifiedKFold
from LogisticRegression import LogisticRegression
scores=[]
skf = StratifiedKFold( y, fold)
for train_index, test_index in skf:
X_train, X_test, y_train, y_test = X[train_index,:], X[test_index,:], y[train_index], y[test_index]
lr = LogisticRegression(learning=solver,weight_decay=wdecay,eta_0=eta, batch_size=bsize)
lr.fit(X_train,y_train)
scores.append(lr.score(X_test,y_test))
return np.mean(scores)示例2: GaussianGenerativeModel
# 需要导入模块: from LogisticRegression import LogisticRegression [as 别名]
# 或者: from LogisticRegression.LogisticRegression import fit [as 别名]
# Do not change anything below this line!!
# -----------------------------------------------------------------
# Read from file and extract X and Y
df = pd.read_csv("fruit.csv")
X = df[['width', 'height']].values
Y = (df['fruit'] - 1).values
nb1 = GaussianGenerativeModel(isSharedCovariance=False)
nb1.fit(X,Y)
nb1.test_insample()
print "Gaussian model with separate covariance: in-sample error rate %.1f%%" % (nb1.insample_err * 100.0 )
nb1.visualize("generative_result_separate_covariances.png",show_charts=True)
nb2 = GaussianGenerativeModel(isSharedCovariance=True)
nb2.fit(X,Y)
nb2.test_insample()
print "Gaussian model with shared covariance: in-sample error rate %.1f%%" % (nb2.insample_err * 100.0 )
nb2.visualize("generative_result_shared_covariances.png",show_charts=True)
for idx,lamda in enumerate([ 0.0001, 0.001, 0.01, 0.1, 1, 0 ]):
lr = LogisticRegression(eta=eta, lambda_parameter=lamda)
lr.fit(X,Y)
lr.test_insample()
print "Logistic regression with lambda %.5f, in-sample error rate %.1f%%" % ( lamda, lr.insample_err * 100.0 )
lr.visualize('logistic_regression_result_%d.png' % idx, show_charts=True)
示例3: read_data
# 需要导入模块: from LogisticRegression import LogisticRegression [as 别名]
# 或者: from LogisticRegression.LogisticRegression import fit [as 别名]
shuffle_examples, normalize_fetures= True , True
X_train,y_train = read_data("./Data/train",normalize_fetures,shuffle_examples)
X_test,y_test = read_data("./Data/test",normalize_fetures)
if doGridSearch:
if regularized:
eta, weight, cv_acc= RLR_SGD_grid(X_train, y_train,bsize)
else:
eta, cv_acc= LR_SGD_grid(X_train, y_train, bsize)
weight=0
else:
eta=0.01
if regularized:
weight=0.01
else:
weight=0
solver="SGD"
# lr = LogisticRegression(learning=solver)
lr = LogisticRegression(learning=solver, eta_0=eta, weight_decay=weight, max_epoch=10, batch_size=bsize)
lr.fit(X_train,y_train)
sklr = linear_model.LogisticRegression(penalty="l2",C=10000.).fit(X_train,y_train)
test_acc=lr.score(X_test,y_test)
runname=str(bsize)+('','R')[regularized]+'LR'
print solver, runname, "Test Accuracy:", test_acc
with open('out','a') as f:
f.write('{0}\t\t{1}\t\t{2}\n'.format(runname, cv_acc, test_acc))
# print "SkitLearn Accuracy:",sklr.score(X_test,y_test)