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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 --------------------------------- */
package com.github.jessemull.microflexbiginteger.stat;
/* ----------------------------- Dependencies ------------------------------- */
import java.math.BigDecimal;
import java.math.MathContext;
import java.util.ArrayList;
import java.util.Collection;
import java.util.List;
import java.util.Map;
import java.util.TreeMap;
import com.google.common.base.Preconditions;
import com.github.jessemull.microflexbiginteger.plate.Plate;
import com.github.jessemull.microflexbiginteger.plate.Well;
import com.github.jessemull.microflexbiginteger.plate.WellSet;
/**
* This class performs weighted statistical operations for BigInteger plate stacks,
* plates, wells and well sets. To create a custom weighted statistical operation
* extend this class and override the calculate methods using the appropriate
* weighted statistical operation.
*
*
*
* 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 abstract class DescriptiveStatisticWeightsContext extends DescriptiveStatisticContext {
/* ---------------- Well statistics for all plate wells ----------------- */
/**
* Returns the weighted statistic for each plate well.
* @param Plate the plate
* @param double[] weights for the data set
* @param MathContext the math context
* @return map of wells and results
*/
public Map plate(Plate plate, double[] weights, MathContext mc) {
Preconditions.checkNotNull(plate, "The plate value cannot be null.");
Map result = new TreeMap();
for (Well well : plate) {
Well clone = new Well(well);
result.put(clone, well(well, weights, mc));
}
return result;
}
/**
* Returns the weighted statistic for each plate well using the values between the
* beginning and ending indices.
* @param Plate the plate
* @param double[] weights for the data set
* @param int beginning index of subset
* @param int length of subset
* @param MathContext the math context
* @return map of wells and results
*/
public Map plate(Plate plate, double[] weights, int begin, int length, MathContext mc) {
Preconditions.checkNotNull(plate, "The plate value cannot be null.");
Map result = new TreeMap();
for (Well well : plate) {
Well clone = new Well(well);
result.put(clone, well(well, weights, begin, length, mc));
}
return result;
}
/* --------------------- Aggregated plate statistics ------------------- */
/**
* Returns the aggregated weighted statistic for the plate.
* @param Plate the plate
* @param double[] weights for the data set
* @param MathContext the math context
* @return the aggregated result
*/
public BigDecimal platesAggregated(Plate plate, double[] weights, MathContext mc) {
Preconditions.checkNotNull(plate, "The plate cannot be null.");
Preconditions.checkNotNull(weights, "Weights array cannot be null.");
List aggregated = new ArrayList();
for (Well well : plate) {
List input = well.toBigDecimal();
for(int i = 0; i < input.size(); i++) {
aggregated.add(input.get(i).multiply(new BigDecimal(weights[i])));
}
}
return calculate(aggregated, mc);
}
/**
* Returns the aggregated weighted statistic for each plate.
* @param Collection collection of plates
* @param double[] weights for the data set
* @param MathContext the math context
* @return map of plates and aggregated results
*/
public Map platesAggregated(
Collection collection, double[] weights, MathContext mc) {
Preconditions.checkNotNull(collection, "The plate collection cannot be null.");
Map results = new TreeMap();
for(Plate plate : collection) {
List aggregated = new ArrayList();
Plate clone = new Plate(plate);
for (Well well : plate) {
List input = well.toBigDecimal();
for(int i = 0; i < input.size(); i++) {
aggregated.add(input.get(i).multiply(new BigDecimal(weights[i])));
}
}
results.put(clone, calculate(aggregated, mc));
}
return results;
}
/**
* Returns the aggregated weighted statistic for each plate.
* @param PlateBigInteger[] array of plates
* @param double[] weights for the data set
* @param MathContext the math context
* @return map of plates and aggregated results
*/
public Map platesAggregated(
Plate[] array, double[] weights, MathContext mc) {
Preconditions.checkNotNull(array, "The plate array cannot be null.");
Map results = new TreeMap();
for(Plate plate : array) {
List aggregated = new ArrayList();
Plate clone = new Plate(plate);
for (Well well : plate) {
List input = well.toBigDecimal();
for(int i = 0; i < input.size(); i++) {
aggregated.add(input.get(i).multiply(new BigDecimal(weights[i])));
}
}
results.put(clone, calculate(aggregated, mc));
}
return results;
}
/**
* Returns the aggregated weighted statistic for the plate using the values
* between the indices.
* @param Plate the plate
* @param double[] weights for the data set
* @param int beginning index of subset
* @param int length of subset
* @param MathContext the math context
* @return the aggregated result
*/
public BigDecimal platesAggregated(Plate plate, double[] weights, int begin, int length, MathContext mc) {
Preconditions.checkNotNull(plate, "The plate cannot be null.");
List aggregated = new ArrayList();
for (Well well : plate) {
List input = well.toBigDecimal().subList(begin, begin + length);
for(int i = 0; i < input.size(); i++) {
aggregated.add(input.get(i).multiply(new BigDecimal(weights[i]), mc));
}
}
return calculate(aggregated, mc);
}
/**
* Returns the aggregated weighted statistic for each plate using the values
* between the indices.
* @param Collection collection of plates
* @param double[] weights for the data set
* @param int beginning index of subset
* @param int length of subset
* @param MathContext the math context
* @return map of plates and aggregated results
*/
public Map platesAggregated(
Collection collection, double[] weights, int begin, int length, MathContext mc) {
Preconditions.checkNotNull(collection, "The plate collection cannot be null.");
Map results = new TreeMap();
for(Plate plate : collection) {
List aggregated = new ArrayList();
Plate clone = new Plate(plate);
for (Well well : plate) {
List input = well.toBigDecimal().subList(begin, begin + length);
for(int i = 0; i < input.size(); i++) {
aggregated.add(input.get(i).multiply(new BigDecimal(weights[i])));
}
}
results.put(clone, calculate(aggregated, mc));
}
return results;
}
/**
* Returns the aggregated weighted statistic for each plate using the values
* between the indices.
* @param PlateBigInteger[] array of plates
* @param double[] weights for the data set
* @param int beginning index of subset
* @param int length of subset
* @param MathContext the math context
* @return map of plates and aggregated results
*/
public Map platesAggregated(
Plate[] array, double[] weights, int begin, int length, MathContext mc) {
Preconditions.checkNotNull(array, "The plate array cannot be null.");
Map results = new TreeMap();
for(Plate plate : array) {
List aggregated = new ArrayList();
Plate clone = new Plate(plate);
for (Well well : plate) {
List input = well.toBigDecimal().subList(begin, begin + length);
for(int i = 0; i < input.size(); i++) {
aggregated.add(input.get(i).multiply(new BigDecimal(weights[i])));
}
}
results.put(clone, calculate(aggregated, mc));
}
return results;
}
/* --------------- Well statistics for all wells in a set -------------- */
/**
* Returns the weighted statistic of each well in the well set.
* @param WellSet the well set
* @param double[] weights for the data set
* @param MathContext the math context
* @return map of wells and results
*/
public Map set(WellSet set, double[] weights, MathContext mc) {
Preconditions.checkNotNull(set, "The set cannot be null.");
Map result = new TreeMap();
for (Well well : set) {
Well clone = new Well(well);
result.put(clone, well(well, weights, mc));
}
return result;
}
/**
* Returns the weighted statistic of each well in the well set using the values
* between the beginning and ending indices.
* @param WellSet the well set
* @param double[] weights for the data set
* @param int beginning index of subset
* @param int length of subset
* @param MathContext the math context
* @return map of wells and results
*/
public Map set(WellSet set, double[] weights, int begin, int length, MathContext mc) {
Preconditions.checkNotNull(set, "The well set cannot be null.");
Map result = new TreeMap();
for (Well well : set) {
Well clone = new Well(well);
result.put(clone, well(well, weights, begin, length, mc));
}
return result;
}
/* --------------------- Aggregated set statistics --------------------- */
/**
* Returns the aggregated weighted statistic for the well set.
* @param WellSet the well set
* @param double[] weights for the data set
* @param MathContext the math context
* @return the aggregated results
*/
public BigDecimal setsAggregated(WellSet set, double[] weights, MathContext mc) {
Preconditions.checkNotNull(set, "The well set cannot be null.");
Preconditions.checkNotNull(weights, "Weights array cannot be null.");
List aggregated = new ArrayList();
for (Well well : set) {
List input = well.toBigDecimal();
for(int i = 0; i < input.size(); i++) {
aggregated.add(input.get(i).multiply(new BigDecimal(weights[i])));
}
}
return calculate(aggregated, mc);
}
/**
* Returns the aggregated weighted statistic for each well set.
* @param Collection collection of well sets
* @param double[] weights for the data set
* @param MathContext the math context
* @return map of well sets and aggregated results
*/
public Map setsAggregated(
Collection collection, double[] weights, MathContext mc) {
Preconditions.checkNotNull(collection, "The well set collection cannot be null.");
Map results = new TreeMap();
for(WellSet set : collection) {
List aggregated = new ArrayList();
WellSet clone = new WellSet(set);
for (Well well : set) {
List input = well.toBigDecimal();
for(int i = 0; i < input.size(); i++) {
aggregated.add(input.get(i).multiply(new BigDecimal(weights[i])));
}
}
results.put(clone, calculate(aggregated, mc));
}
return results;
}
/**
* Returns the aggregated weighted statistic for each well set.
* @param WellSetBigInteger[] array of well sets
* @param double[] weights for the data set
* @param MathContext the math context
* @return map of well sets and aggregated results
*/
public Map setsAggregated(
WellSet[] array, double[] weights, MathContext mc) {
Preconditions.checkNotNull(array, "The well set array cannot be null.");
Map results = new TreeMap();
for(WellSet set : array) {
List aggregated = new ArrayList();
WellSet clone = new WellSet(set);
for (Well well : set) {
List input = well.toBigDecimal();
for(int i = 0; i < input.size(); i++) {
aggregated.add(input.get(i).multiply(new BigDecimal(weights[i])));
}
}
results.put(clone, calculate(aggregated, mc));
}
return results;
}
/**
* Returns the aggregated weighted statistic for the well set using the values
* between the indices.
* @param WellSet the well set
* @param double[] weights for the data set
* @param int beginning index of subset
* @param int length of subset
* @param MathContext the math context
* @return the aggregated result
*/
public BigDecimal setsAggregated(WellSet set, double[] weights, int begin, int length, MathContext mc) {
Preconditions.checkNotNull(set, "The well set cannot be null.");
List aggregated = new ArrayList();
for (Well well : set) {
List input = well.toBigDecimal().subList(begin, begin + length);
for(int i = 0; i < input.size(); i++) {
aggregated.add(input.get(i).multiply(new BigDecimal(weights[i])));
}
}
return calculate(aggregated, mc);
}
/**
* Returns the aggregated weighted statistic for each well set using the values
* between the indices.
* @param Collection collection of well sets
* @param double[] weights for the data set
* @param int beginning index of subset
* @param int length of subset
* @param MathContext the math context
* @return map of well sets and aggregated results
*/
public Map setsAggregated(
Collection collection, double[] weights, int begin, int length, MathContext mc) {
Preconditions.checkNotNull(collection, "The well set collection cannot be null.");
Map results = new TreeMap();
for(WellSet set : collection) {
List aggregated = new ArrayList();
WellSet clone = new WellSet(set);
for (Well well : set) {
List input = well.toBigDecimal().subList(begin, begin + length);
for(int i = 0; i < input.size(); i++) {
aggregated.add(input.get(i).multiply(new BigDecimal(weights[i])));
}
}
results.put(clone, calculate(aggregated, mc));
}
return results;
}
/**
* Returns the aggregated weighted statistic for each well set using the values
* between the indices.
* @param WellSetBigInteger[] array of well sets
* @param double[] weights for the data set
* @param int beginning index of subset
* @param int length of subset
* @param MathContext the math context
* @return map of well sets and aggregated results
*/
public Map setsAggregated(
WellSet[] array, double[] weights, int begin, int length, MathContext mc) {
Preconditions.checkNotNull(array, "The well set array cannot be null.");
Map results = new TreeMap();
for(WellSet set : array) {
List aggregated = new ArrayList();
WellSet clone = new WellSet(set);
for (Well well : set) {
List input = well.toBigDecimal().subList(begin, begin + length);
for(int i = 0; i < input.size(); i++) {
aggregated.add(input.get(i).multiply(new BigDecimal(weights[i])));
}
}
results.put(clone, calculate(aggregated, mc));
}
return results;
}
/* -------------------------- Well statistics -------------------------- */
/**
* Returns the weighted well statistic.
* @param Well the well
* @param double[] weights for the data set
* @param MathContext the math context
* @return the result
*/
public BigDecimal well(Well well, double[] weights, MathContext mc) {
Preconditions.checkNotNull(well, "The well cannot be null.");
Preconditions.checkNotNull(weights, "The weights array cannot be null.");
return calculate(well.toBigDecimal(), weights, mc);
}
/**
* Returns the well weighted statistic for the values between the beginning and
* ending indices.
* @param Well the well
* @param double[] weights for the data set
* @param int beginning index of subset
* @param int length of the subset
* @param MathContext the math context
* @return the result
*/
public BigDecimal well(Well well, double[] weights, int begin, int length, MathContext mc) {
Preconditions.checkNotNull(well, "The well cannot be null.");
Preconditions.checkNotNull(weights, "The weights array cannot be null.");
Preconditions.checkArgument(begin <= well.toBigDecimal().size() &&
begin >= 0 &&
begin + length <= well.toBigDecimal().size());
return calculate(well.toBigDecimal(), weights, begin, length, mc);
}
/* -------- Methods for calculating the statistic of a data set -------- */
/**
* Calculates the weighted statistic.
* @param List the list
* @param double[] weights for the data set
* @param MathContext the math context
* @return the result
*/
public abstract BigDecimal calculate(List list, double[] weights, MathContext mc);
/**
* Calculates the weighted statistic 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 abstract BigDecimal calculate(List list, double[] weights, int begin, int length, MathContext mc);
}
