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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* 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.
*/
package spark.partial
import cern.jet.stat.Probability
/**
* A utility class for caching Student's T distribution values for a given confidence level
* and various sample sizes. This is used by the MeanEvaluator to efficiently calculate
* confidence intervals for many keys.
*/
private[spark] class StudentTCacher(confidence: Double) {
val NORMAL_APPROX_SAMPLE_SIZE = 100 // For samples bigger than this, use Gaussian approximation
val normalApprox = Probability.normalInverse(1 - (1 - confidence) / 2)
val cache = Array.fill[Double](NORMAL_APPROX_SAMPLE_SIZE)(-1.0)
def get(sampleSize: Long): Double = {
if (sampleSize >= NORMAL_APPROX_SAMPLE_SIZE) {
normalApprox
} else {
val size = sampleSize.toInt
if (cache(size) < 0) {
cache(size) = Probability.studentTInverse(1 - confidence, size - 1)
}
cache(size)
}
}
}
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