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Java example source code file (pascalTestCases)

This example Java source code file (pascalTestCases) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

apache, asf, density, distribution, failed, false, inverse, license, nan, succeeded, values, width, you

The pascalTestCases Java example source code

# 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.
#
#------------------------------------------------------------------------------
# R source file to validate Pascal distribution tests in
# org.apache.commons.math.distribution.PascalDistributionTest
#
# To run the test, install R, put this file and testFunctions
# into the same directory, launch R from this directory and then enter
# source("<name-of-this-file>")
#
# R functions used
# dnbinom(x, size, prob, mu, log = FALSE) <- density
# pnbinom(q, size, prob, mu, lower.tail = TRUE, log.p = FALSE) <- distribution
# qnbinom(p, size, prob, mu, lower.tail = TRUE, log.p = FALSE) <- quantiles
#------------------------------------------------------------------------------
tol <- 1E-9                       # error tolerance for tests
#------------------------------------------------------------------------------
# Function definitions

source("testFunctions")           # utility test functions

# function to verify density computations

verifyDensity <- function(points, expected, size, p, tol) {
    rDensityValues <- rep(0, length(points))
    i <- 0
    for (point in points) {
        i <- i + 1
        rDensityValues[i] <- dnbinom(point, size, p)
    }
    output <- c("Density test size = ", size, ", p = ", p)
    if (assertEquals(expected,rDensityValues,tol,"Density Values")) {
        displayPadded(output, SUCCEEDED, WIDTH)
    } else {
        displayPadded(output, FAILED, WIDTH)
    }
}

# function to verify distribution computations

verifyDistribution <- function(points, expected, size, p, tol) {
    rDistValues <- rep(0, length(points))
    i <- 0
    for (point in points) {
        i <- i + 1
        rDistValues[i] <- pnbinom(point, size, p)
    }
    output <- c("Distribution test size = ", size, ", p = ", p)
    if (assertEquals(expected,rDistValues,tol,"Distribution Values")) {
        displayPadded(output, SUCCEEDED, WIDTH)
    } else {
        displayPadded(output, FAILED, WIDTH)
    }
}

#--------------------------------------------------------------------------
cat("Negative Binomial test cases\n")

size <- 10.0
probability <- 0.70

densityPoints <- c(-1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
densityValues <- c(0, 0.0282475249, 0.0847425747, 0.139825248255, 0.167790297906, 0.163595540458,
          0.137420253985, 0.103065190489, 0.070673273478, 0.0450542118422, 0.0270325271053,
          0.0154085404500, 0.0084046584273)
distributionValues <- c(0, 0.0282475249, 0.1129900996, 0.252815347855, 0.420605645761, 0.584201186219,
          0.721621440204, 0.824686630693, 0.895359904171, 0.940414116013, 0.967446643119,
          0.982855183569, 0.991259841996)
inverseCumPoints <- c( 0, 0.001, 0.010, 0.025, 0.050, 0.100, 0.999,
          0.990, 0.975, 0.950, 0.900)
inverseCumValues <- c(-1, -1, -1, -1, 0, 0, 13, 10, 9, 8, 7)

verifyDensity(densityPoints,densityValues,size,probability,tol)
verifyDistribution(densityPoints, distributionValues, size, probability, tol)

i <- 0
rInverseCumValues <- rep(0,length(inverseCumPoints))
for (point in inverseCumPoints) {
  i <- i + 1
  rInverseCumValues[i] <- qnbinom(point, size, probability)
}

output <- c("Inverse Distribution test n = ", size, ", p = ", probability)
# R defines quantiles from the right, need to subtract one
if (assertEquals(inverseCumValues, rInverseCumValues-1, tol,
    "Inverse Dist Values")) {
    displayPadded(output, SUCCEEDED, 80)
} else {
    displayPadded(output, FAILED, 80)
}

# Degenerate cases

size <- 5
probability <- 0.0

densityPoints <- c(-1, 0, 1, 10, 11)
# Note: commons math returns 0's below
densityValues <- c(NaN, NaN, NaN, NaN, NaN)
distributionPoints <- c(-1, 0, 1, 5, 10)
# Note: commons math returns 0's below
distributionValues <- c(NaN, NaN, NaN, NaN, NaN)

output <- c("Density test n = ", size, ", p = ", probability)
verifyDensity(densityPoints,densityValues,size,probability,tol)
output <- c("Distribution test n = ", size, ", p = ", probability)
verifyDistribution(distributionPoints,distributionValues,size,probability,tol)

size <- 5
probability <- 1.0

densityPoints <- c(-1, 0, 1, 2, 5, 10)
densityValues <- c(0, 1, 0, 0, 0, 0)
distributionPoints <- c(-1, 0, 1, 2, 5, 10)
distributionValues <- c(0, 1, 1, 1, 1, 1)

output <- c("Density test n = ", size, ", p = ", probability)
verifyDensity(densityPoints,densityValues,size,probability,tol)
output <- c("Distribution test n = ", size, ", p = ", probability)
verifyDistribution(distributionPoints,distributionValues,size,probability,tol)

displayDashes(WIDTH)

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