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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#------------------------------------------------------------------------------
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The purpose of the R programs included in this directory is to validate
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the target values used in Apache commons math unit tests. Success running the
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R and commons-math tests on a platform (OS and R version) means that R and
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commons-math give results for the test cases that are close in value. The
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tests include configurable tolerance levels; but care must be taken in changing
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these, since in most cases the pre-set tolerance is close to the number of
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decimal digits used in expressing the expected values (both here and in the
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corresponding commons-math unit tests).
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Of course it is always possible that both R and commons-math give incorrect
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values for test cases, so these tests should not be interpreted as definitive
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in any absolute sense. The value of developing and running the tests is really
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to generate questions (and answers!) when the two systems give different
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Contributions of additional test cases (both R and Junit code) or just
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R programs to validate commons-math tests that are not covered here would be
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0) Download and install R. You can get R here
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http://www.r-project.org/
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Follow the install instructions and make sure that you can launch R from this
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(i.e., either explitly add R to your OS path or let the install package do it
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1) Launch R from this directory and type
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to an R prompt. This should produce output to the console similar to this:
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Density test n = 10, p = 0.7...........................................SUCCEEDED
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Distribution test n = 10, p = 0.7......................................SUCCEEDED
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Inverse Distribution test n = 10, p = 0.7..............................SUCCEEDED
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Density test n = 5, p = 0..............................................SUCCEEDED
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Distribution test n = 5, p = 0.........................................SUCCEEDED
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Density test n = 5, p = 1..............................................SUCCEEDED
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Distribution test n = 5, p = 1.........................................SUCCEEDED
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--------------------------------------------------------------------------------
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Distribution test mu = 2.1, sigma = 1.4................................SUCCEEDED
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Distribution test mu = 2.1, sigma = 1.4................................SUCCEEDED
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Distribution test mu = 0, sigma = 1....................................SUCCEEDED
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Distribution test mu = 0, sigma = 0.1..................................SUCCEEDED
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--------------------------------------------------------------------------------
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WORKING WITH THE TESTS
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The R distribution comes with online manuals that you can view by launching
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a browser instance and then entering
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at an R prompt. Poking about in the test case files and the online docs should
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bring you up to speed fairly quickly. Here are some basic things to get
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you started. I should note at this point that I am by no means an expert R
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programmer, so some things may not be implemented in the the nicest way.
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Comments / suggestions for improvement are welcome!
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All of the test cases use some basic functions and global constants (screen
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width and success / failure strings) defined in "testFunctions." The
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R "source" function is used to "import" these functions into each of the test
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programs. The "testAll" program pulls together and executes all of the
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individual test programs. You can execute any one of them by just entering
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> source(<program-name>).
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The "assertEquals" function in the testFunctions file mimics the similarly
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named function used by Junit:
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assertEquals <- function(expected, observed, tol, message) {
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if(any(abs(expected - observed) > tol)) {
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cat("FAILURE: ",message,"\n")
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cat("EXPECTED: ",expected,"\n")
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cat("OBSERVED: ",observed,"\n")
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The <expected> and <observed> arguments can be scalar values, vectors or
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matrices. If the arguments are vectors or matrices, corresponding entries
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The standard pattern used throughout the tests looks like this (from
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Start by defining a "verification function" -- in this example a function to
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verify computation of binomial probabilities. The <points> argument is a vector
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of integer values to feed into the density function, <expected> is a vector of
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the computed probabilies from the commons-math Junit tests, <n> and <p> are
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parameters of the distribution and <tol> is the error tolerance of the test.
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The function computes the probabilities using R and compares the values that
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R produces with those in the <expected> vector.
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verifyDensity <- function(points, expected, n, p, tol) {
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rDensityValues <- rep(0, length(points))
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for (point in points) {
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rDensityValues[i] <- dbinom(point, n, p, log = FALSE)
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output <- c("Density test n = ", n, ", p = ", p)
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if (assertEquals(expected,rDensityValues,tol,"Density Values")) {
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displayPadded(output, SUCCEEDED, WIDTH)
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displayPadded(output, FAILED, WIDTH)
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The displayPadded function just displays its first and second arguments with
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enough dots in between to make the whole string WIDTH characters long. It is
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defined in testFunctions.
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Then call this function with different parameters corresponding to the different
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densityPoints <- c(-1,0,1,2,3,4,5,6,7,8,9,10,11)
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densityValues <- c(0, 0.0000, 0.0001, 0.0014, 0.0090, 0.0368, 0.1029,
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0.2001, 0.2668, 0.2335, 0.1211, 0.0282, 0)
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verifyDensity(densityPoints, densityValues, size, probability, tol)
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If the values computed by R match the target values in densityValues, this will
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produce one line of output to the console:
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Density test n = 10, p = 0.7...........................................SUCCEEDED
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If you modify the value of tol set at the top of binomialTestCases to make the
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test more sensitive than the number of digits specified in the densityValues
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vector, it will fail, producing the following output, showing the failure and
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the expected and observed values:
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FAILURE: Density Values
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EXPECTED: 0 0 1e-04 0.0014 0.009 0.0368 0.1029 0.2001 0.2668 0.2335 0.1211 /
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OBSERVED: 0 5.9049e-06 0.000137781 0.0014467005 0.009001692 0.036756909 /
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0.1029193452 0.200120949 0.266827932 0.2334744405 0.121060821 0.0282475249 0
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Density test n = 10, p = 0.7..............................................FAILED
added
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src/test/R/README.txt
