本文整理汇总了Python中pyspark.mllib.regression._regression_train_wrapper函数的典型用法代码示例。如果您正苦于以下问题：Python _regression_train_wrapper函数的具体用法？Python _regression_train_wrapper怎么用？Python _regression_train_wrapper使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了_regression_train_wrapper函数的5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，您的评价将有助于我们的系统推荐出更棒的Python代码示例。
示例1: train
def train(cls, data, iterations=100, step=1.0, regParam=0.01,
miniBatchFraction=1.0, initialWeights=None, regType="l2", intercept=False):
"""
Train a support vector machine on the given data.
:param data: The training data, an RDD of LabeledPoint.
:param iterations: The number of iterations (default: 100).
:param step: The step parameter used in SGD
(default: 1.0).
:param regParam: The regularizer parameter (default: 0.01).
:param miniBatchFraction: Fraction of data to be used for each SGD
iteration.
:param initialWeights: The initial weights (default: None).
:param regType: The type of regularizer used for training
our model.
:Allowed values:
- "l1" for using L1 regularization
- "l2" for using L2 regularization
- None for no regularization
(default: "l2")
:param intercept: Boolean parameter which indicates the use
or not of the augmented representation for
training data (i.e. whether bias features
are activated or not).
"""
def train(rdd, i):
return callMLlibFunc("trainSVMModelWithSGD", rdd, int(iterations), float(step),
float(regParam), float(miniBatchFraction), i, regType,
bool(intercept))
return _regression_train_wrapper(train, SVMModel, data, initialWeights)
开发者ID:OspreyX，项目名称:spark，代码行数:34，代码来源:classification.py
示例2: train
def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0,
initialWeights=None, regParam=1.0, regType="none", intercept=False):
"""
Train a logistic regression model on the given data.
:param data: The training data.
:param iterations: The number of iterations (default: 100).
:param step: The step parameter used in SGD
(default: 1.0).
:param miniBatchFraction: Fraction of data to be used for each SGD
iteration.
:param initialWeights: The initial weights (default: None).
:param regParam: The regularizer parameter (default: 1.0).
:param regType: The type of regularizer used for training
our model.
:Allowed values:
- "l1" for using L1Updater
- "l2" for using SquaredL2Updater
- "none" for no regularizer
(default: "none")
@param intercept: Boolean parameter which indicates the use
or not of the augmented representation for
training data (i.e. whether bias features
are activated or not).
"""
def train(rdd, i):
return callMLlibFunc("trainLogisticRegressionModelWithSGD", rdd, iterations, step,
miniBatchFraction, i, regParam, regType, intercept)
return _regression_train_wrapper(train, LogisticRegressionModel, data, initialWeights)
开发者ID:BViki，项目名称:spark，代码行数:33，代码来源:classification.py
示例3: train
def train(cls, data, iterations=100, step=1.0, regParam=1.0,
miniBatchFraction=1.0, initialWeights=None, regType="none", intercept=False):
"""
Train a support vector machine on the given data.
@param data: The training data.
@param iterations: The number of iterations (default: 100).
@param step: The step parameter used in SGD
(default: 1.0).
@param regParam: The regularizer parameter (default: 1.0).
@param miniBatchFraction: Fraction of data to be used for each SGD
iteration.
@param initialWeights: The initial weights (default: None).
@param regType: The type of regularizer used for training
our model.
Allowed values: "l1" for using L1Updater,
"l2" for using
SquaredL2Updater,
"none" for no regularizer.
(default: "none")
@param intercept: Boolean parameter which indicates the use
or not of the augmented representation for
training data (i.e. whether bias features
are activated or not).
"""
sc = data.context
def train(jrdd, i):
return sc._jvm.PythonMLLibAPI().trainSVMModelWithSGD(
jrdd, iterations, step, regParam, miniBatchFraction, i, regType, intercept)
return _regression_train_wrapper(sc, train, SVMModel, data, initialWeights)
开发者ID:4T-Shirt，项目名称:spark，代码行数:32，代码来源:classification.py
示例4: train
def train(cls, data, iterations=100, initialWeights=None, regParam=0.01, regType="l2",
intercept=False, corrections=10, tolerance=1e-4, validateData=True, numClasses=2):
"""
Train a logistic regression model on the given data.
:param data: The training data, an RDD of LabeledPoint.
:param iterations: The number of iterations (default: 100).
:param initialWeights: The initial weights (default: None).
:param regParam: The regularizer parameter (default: 0.01).
:param regType: The type of regularizer used for training
our model.
:Allowed values:
- "l1" for using L1 regularization
- "l2" for using L2 regularization
- None for no regularization
(default: "l2")
:param intercept: Boolean parameter which indicates the use
or not of the augmented representation for
training data (i.e. whether bias features
are activated or not).
:param corrections: The number of corrections used in the LBFGS
update (default: 10).
:param tolerance: The convergence tolerance of iterations for
L-BFGS (default: 1e-4).
:param validateData: Boolean parameter which indicates if the
algorithm should validate data before training.
(default: True)
:param numClasses: The number of classes (i.e., outcomes) a label can take
in Multinomial Logistic Regression (default: 2).
>>> data = [
... LabeledPoint(0.0, [0.0, 1.0]),
... LabeledPoint(1.0, [1.0, 0.0]),
... ]
>>> lrm = LogisticRegressionWithLBFGS.train(sc.parallelize(data), iterations=10)
>>> lrm.predict([1.0, 0.0])
1
>>> lrm.predict([0.0, 1.0])
0
"""
def train(rdd, i):
return callMLlibFunc("trainLogisticRegressionModelWithLBFGS", rdd, int(iterations), i,
float(regParam), regType, bool(intercept), int(corrections),
float(tolerance), bool(validateData), int(numClasses))
if initialWeights is None:
if numClasses == 2:
initialWeights = [0.0] * len(data.first().features)
else:
if intercept:
initialWeights = [0.0] * (len(data.first().features) + 1) * (numClasses - 1)
else:
initialWeights = [0.0] * len(data.first().features) * (numClasses - 1)
return _regression_train_wrapper(train, LogisticRegressionModel, data, initialWeights)
开发者ID:1ambda，项目名称:spark，代码行数:57，代码来源:classification.py
示例5: train
def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0,
initialWeights=None, regParam=0.01, regType="l2", intercept=False,
validateData=True, convergenceTol=0.001):
"""
Train a logistic regression model on the given data.
:param data:
The training data, an RDD of LabeledPoint.
:param iterations:
The number of iterations.
(default: 100)
:param step:
The step parameter used in SGD.
(default: 1.0)
:param miniBatchFraction:
Fraction of data to be used for each SGD iteration.
(default: 1.0)
:param initialWeights:
The initial weights.
(default: None)
:param regParam:
The regularizer parameter.
(default: 0.01)
:param regType:
The type of regularizer used for training our model.
Supported values:
- "l1" for using L1 regularization
- "l2" for using L2 regularization (default)
- None for no regularization
:param intercept:
Boolean parameter which indicates the use or not of the
augmented representation for training data (i.e., whether bias
features are activated or not).
(default: False)
:param validateData:
Boolean parameter which indicates if the algorithm should
validate data before training.
(default: True)
:param convergenceTol:
A condition which decides iteration termination.
(default: 0.001)
"""
warnings.warn(
"Deprecated in 2.0.0. Use ml.classification.LogisticRegression or "
"LogisticRegressionWithLBFGS.")
def train(rdd, i):
return callMLlibFunc("trainLogisticRegressionModelWithSGD", rdd, int(iterations),
float(step), float(miniBatchFraction), i, float(regParam), regType,
bool(intercept), bool(validateData), float(convergenceTol))
return _regression_train_wrapper(train, LogisticRegressionModel, data, initialWeights)
开发者ID:AllenShi，项目名称:spark，代码行数:53，代码来源:classification.py
注：本文中的pyspark.mllib.regression._regression_train_wrapper函数示例由纯净天空整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。 |
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