本文整理汇总了Python中pyspark.mllib.stat.Statistics.kolmogorovSmirnovTest方法的典型用法代码示例。如果您正苦于以下问题：Python Statistics.kolmogorovSmirnovTest方法的具体用法？Python Statistics.kolmogorovSmirnovTest怎么用？Python Statistics.kolmogorovSmirnovTest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pyspark.mllib.stat.Statistics的用法示例。
在下文中一共展示了Statistics.kolmogorovSmirnovTest方法的2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: test_R_implementation_equivalence
# 需要导入模块: from pyspark.mllib.stat import Statistics [as 别名]
# 或者: from pyspark.mllib.stat.Statistics import kolmogorovSmirnovTest [as 别名]
def test_R_implementation_equivalence(self):
data = self.sc.parallelize([
1.1626852897838, -0.585924465893051, 1.78546500331661, -1.33259371048501,
-0.446566766553219, 0.569606122374976, -2.88971761441412, -0.869018343326555,
-0.461702683149641, -0.555540910137444, -0.0201353678515895, -0.150382224136063,
-0.628126755843964, 1.32322085193283, -1.52135057001199, -0.437427868856691,
0.970577579543399, 0.0282226444247749, -0.0857821886527593, 0.389214404984942
])
model = Statistics.kolmogorovSmirnovTest(data, "norm")
self.assertAlmostEqual(model.statistic, 0.189, 3)
self.assertAlmostEqual(model.pValue, 0.422, 3)
model = Statistics.kolmogorovSmirnovTest(data, "norm", 0, 1)
self.assertAlmostEqual(model.statistic, 0.189, 3)
self.assertAlmostEqual(model.pValue, 0.422, 3)示例2: SparkContext
# 需要导入模块: from pyspark.mllib.stat import Statistics [as 别名]
# 或者: from pyspark.mllib.stat.Statistics import kolmogorovSmirnovTest [as 别名]
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from __future__ import print_function
from pyspark import SparkContext
# $example on$
from pyspark.mllib.stat import Statistics
# $example off$
if __name__ == "__main__":
sc = SparkContext(appName="HypothesisTestingKolmogorovSmirnovTestExample")
# $example on$
parallelData = sc.parallelize([0.1, 0.15, 0.2, 0.3, 0.25])
# run a KS test for the sample versus a standard normal distribution
testResult = Statistics.kolmogorovSmirnovTest(parallelData, "norm", 0, 1)
# summary of the test including the p-value, test statistic, and null hypothesis
# if our p-value indicates significance, we can reject the null hypothesis
# Note that the Scala functionality of calling Statistics.kolmogorovSmirnovTest with
# a lambda to calculate the CDF is not made available in the Python API
print(testResult)
# $example off$
sc.stop()