Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance. Project price only 1 $
You can buy this project and download/modify it how often you want.
/**
* 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 -------------------------------- */
package com.github.jessemull.microflexbiginteger.stat;
/* ----------------------------- Dependencies ------------------------------ */
import java.math.BigDecimal;
import java.math.MathContext;
import java.util.ArrayList;
import java.util.List;
/**
* This class calculates the sample variance of BigInteger plate stacks, plates,
* wells and well sets as the sum of the second moment divided by the number of
* values less one.
*
*
*
* From wikipedia: a weight function is a mathematical device used when performing
* a sum, integral, or average to give some elements more "weight" or influence on
* the result than other elements in the same set. In statistics a weighted function
* is often used to correct bias. The weighted statistic class implements a weighted
* function by accepting an array of values as weights. The values in each well of
* the stack, plate, set or well are multiplied by the values within the double
* array prior to the statistical calculation.
*
*
*
* Weighted statistical operations can be performed on stacks, plates, sets and
* wells using standard or aggregated functions. Standard functions calculate the
* desired statistic for each well in the stack, plate or set. Aggregated functions
* aggregate the values from all the wells in the stack, plate or set and perform
* the weighted statistical operation on the aggregated values. Both standard and
* aggregated functions can be performed on a subset of data within the stack,
* plate, set or well.
*
*
*
* The methods within the MicroFlex library are meant to be flexible and the
* descriptive statistic object supports operations using a single stack, plate,
* set or well as well as collections and arrays of stacks, plates, sets or wells.
*
*
*
Operation
*
Beginning Index
*
Length of Subset
*
Input/Output
*
*
*
*
*
Standard
*
*
*
*
*
*
*
+/-
*
*
*
*
*
*
*
+/-
*
*
*
*
*
*
*
Accepts a single well, set, plate or stack and an array of weights as input
*
*
*
Multiplies the values in each well of a well, set, plate or stack by the values
* in the weights array then calculates the statistic using the weighted values
*
*
*
*
*
*
*
*
*
Aggregated
*
*
*
*
*
*
*
+/-
*
*
*
*
*
*
*
+/-
*
*
*
*
*
*
*
Accepts a single well/set/plate/stack or a collection/array of wells/sets/plates/stacks
* and an array of weights as input
*
*
*
Multiplies the values in each well of a well, set, plate or stack by the values in the
* weights array, aggregates the data from all the wells in the well/set/plate/stack then
* calculates the statistic using the aggregated weighted values
*
*
*
*
*
*
* @author Jesse L. Mull
* @update Updated Oct 18, 2016
* @address http://www.jessemull.com
* @email [email protected]
*/
public class SampleVariance extends DescriptiveStatisticWeightsContext {
/**
* Calculates the sample variance.
* @param List the list
* @param MathContext the math context
* @return the result
*/
public BigDecimal calculate(List list, MathContext mc) {
BigDecimal mean = this.mean(list, mc);
BigDecimal sum = BigDecimal.ZERO;
for(BigDecimal db : list) {
sum = sum.add(db.subtract(mean, mc).pow(2, mc), mc);
}
return sum.divide(BigDecimal.valueOf(list.size() - 1), mc);
}
/**
* Calculates the weighted sample variance.
* @param List the list
* @param double[] weights for the data set
* @param MathContext the math context
* @return the result
*/
public BigDecimal calculate(List list, double[] weights, MathContext mc) {
List weighted = new ArrayList();
for(int i = 0; i < list.size(); i++) {
weighted.add(list.get(i).multiply(new BigDecimal(weights[i] + ""), mc));
}
BigDecimal mean = this.mean(weighted, mc);
BigDecimal sum = BigDecimal.ZERO;
for(BigDecimal db : weighted) {
sum = sum.add(db.subtract(mean, mc).pow(2, mc), mc);
}
return sum.divide(BigDecimal.valueOf(list.size() - 1), mc);
}
/**
* Calculates the sample variance of the values between the beginning
* and ending indices.
* @param List the list
* @param int beginning index of subset
* @param int length of subset
* @param MathContext the math context
* @return the result
*/
public BigDecimal calculate(List list, int begin, int length, MathContext mc) {
return calculate(list.subList(begin, begin + length), mc);
}
/**
* Calculates the weighted sample variance of the values between the
* beginning and ending indices.
* @param List the list
* @param double[] weights of the data set
* @param int beginning index of subset
* @param int length of subset
* @param MathContext the math context
* @return the result
*/
public BigDecimal calculate(List list, double[] weights, int begin, int length, MathContext mc) {
return calculate(list.subList(begin, begin + length), weights, mc);
}
/**
* Calculates the mean.
* @param List the list
* @param MathContext the math context
* @return the result
*/
public BigDecimal mean(List list, MathContext mc) {
BigDecimal sum = BigDecimal.ZERO;
for(BigDecimal db : list) {
sum = sum.add(db, mc);
}
return sum.divide(BigDecimal.valueOf(list.size()), mc);
}
}
