Add Random unit tests.

https://codereview.appspot.com/7306066/


git-svn-id: http://skia.googlecode.com/svn/trunk@7670 2bbb7eff-a529-9590-31e7-b0007b416f81

2 files changed, 197 insertions, 0 deletions

diff --git a/gyp/tests.gyp b/gyp/tests.gyp
index 10e99d274d..082336fbd5 100644
--- a/gyp/tests.gyp
+++ b/gyp/tests.gyp
@@ -77,6 +77,7 @@
         '../tests/PointTest.cpp',
         '../tests/PremulAlphaRoundTripTest.cpp',
         '../tests/QuickRejectTest.cpp',
+        '../tests/RandomTest.cpp',
         '../tests/Reader32Test.cpp',
         '../tests/ReadPixelsTest.cpp',
         '../tests/ReadWriteAlphaTest.cpp',
diff --git a/tests/RandomTest.cpp b/tests/RandomTest.cpp
new file mode 100644
index 0000000000..ce75bf3a97
--- /dev/null
+++ b/tests/RandomTest.cpp
@@ -0,0 +1,196 @@
+
+/*
+ * Copyright 2013 Google Inc.
+ *
+ * Use of this source code is governed by a BSD-style license that can be
+ * found in the LICENSE file.
+ */
+
+#include "Test.h"
+#include "SkRandom.h"
+#include "SkTSort.h"
+
+bool anderson_darling_test(double p[32]) {
+    // Min and max Anderson-Darling values allowable for k=32
+    const double kADMin32 = 0.202;        // p-value of ~0.1
+    const double kADMax32 = 3.89;         // p-value of ~0.99
+
+    // sort p values
+    SkTQSort<double>(p, p + 31);
+
+    // and compute Anderson-Darling statistic to ensure these are uniform
+    double s = 0.0;
+    for(int k = 0; k < 32; k++) {
+        double v = p[k]*(1.0 - p[31-k]);
+        if (v < 1.0e-30) {
+           v = 1.0e-30;
+        }
+        s += (2.0*(k+1)-1.0)*log(v);
+    }
+    double a2 = -32.0 - 0.03125*s;
+
+    return (kADMin32 < a2 && a2 < kADMax32);
+}
+
+bool chi_square_test(int bins[256], int e) {
+    // Min and max chisquare values allowable
+    const double kChiSqMin256 = 206.3179;        // probability of chance = 0.99 with k=256
+    const double kChiSqMax256 = 311.5603;        // probability of chance = 0.01 with k=256
+
+    // compute chi-square
+    double chi2 = 0.0;
+    for (int j = 0; j < 256; ++j) {
+        double delta = bins[j] - e;
+        chi2 += delta*delta/e;
+    }
+
+    return (kChiSqMin256 < chi2 && chi2 < kChiSqMax256);
+}
+
+// Approximation to the normal distribution CDF
+// From Waissi and Rossin, 1996
+double normal_cdf(double z) {
+    double t = ((-0.0004406*z*z* + 0.0418198)*z*z + 0.9)*z;
+    t *= -1.77245385091;  // -sqrt(PI)
+    double p = 1.0/(1.0 + exp(t));
+
+    return p;
+}
+
+void test_random_byte(skiatest::Reporter* reporter, int shift) {
+    int bins[256];
+    memset(bins, 0, sizeof(int)*256);
+
+    SkMWCRandom rand;
+    for (int i = 0; i < 256*10000; ++i) {
+        bins[(rand.nextU() >> shift) & 0xff]++;
+    }
+    
+    REPORTER_ASSERT(reporter, chi_square_test(bins, 10000));
+}
+
+void test_random_float(skiatest::Reporter* reporter) {
+    int bins[256];
+    memset(bins, 0, sizeof(int)*256);
+
+    SkMWCRandom rand;
+    for (int i = 0; i < 256*10000; ++i) {
+        float f = rand.nextF();
+        REPORTER_ASSERT(reporter, 0.0f <= f && f < 1.0f);
+        bins[(int)(f*256.f)]++;
+    }
+    REPORTER_ASSERT(reporter, chi_square_test(bins, 10000));
+
+    double p[32];
+    for (int j = 0; j < 32; ++j) {
+        float f = rand.nextF();
+        REPORTER_ASSERT(reporter, 0.0f <= f && f < 1.0f);
+        p[j] = f;
+    }
+    REPORTER_ASSERT(reporter, anderson_darling_test(p));
+}
+
+// This is a test taken from tuftests by Marsaglia and Tsang. The idea here is that
+// we are using the random bit generated from a single shift position to generate
+// "strings" of 16 bits in length, shifting the string and adding a new bit with each 
+// iteration. We track the numbers generated. The ones that we don't generate will
+// have a normal distribution with mean ~24108 and standard deviation ~127. By 
+// creating a z-score (# of deviations from the mean) for one iteration of this step
+// we can determine its probability.
+//
+// The original test used 26 bit strings, but is somewhat slow. This version uses 16 
+// bits which is less rigorous but much faster to generate.
+double test_single_gorilla(skiatest::Reporter* reporter, int shift) {
+    const int kWordWidth = 16;
+    const double kMean = 24108.0;
+    const double kStandardDeviation = 127.0;
+    const int kN = (1 << kWordWidth);
+    const int kNumEntries = kN >> 5;  // dividing by 32
+    unsigned int entries[kNumEntries];
+
+    SkMWCRandom rand;
+    memset(entries, 0, sizeof(unsigned int)*kNumEntries);
+    // pre-seed our string value
+    int value = 0;
+    for (int i = 0; i < kWordWidth-1; ++i) {
+        value <<= 1;
+        unsigned int rnd = rand.nextU();
+        value |= ((rnd >> shift) & 0x1);
+    }
+
+    // now make some strings and track them
+    for (int i = 0; i < kN; ++i) {
+        value <<= 1;
+        unsigned int rnd = rand.nextU();
+        value |= ((rnd >> shift) & 0x1);
+
+        int index = value & (kNumEntries-1);
+        SkASSERT(index < kNumEntries);
+        int entry_shift = (value >> (kWordWidth-5)) & 0x3f;
+        entries[index] |= (0x1 << entry_shift);
+    }
+
+    // count entries
+    int total = 0;
+    for (int i = 0; i < kNumEntries; ++i) {
+        unsigned int entry = entries[i];
+        while (entry) {
+            total += (entry & 0x1);
+            entry >>= 1;
+        }
+    }
+
+    // convert counts to normal distribution z-score
+    double z = ((kN-total)-kMean)/kStandardDeviation;    
+
+    // compute probability from normal distibution CDF 
+    double p = normal_cdf(z);
+
+    REPORTER_ASSERT(reporter, 0.01 < p && p < 0.99);
+    return p;
+}
+
+void test_gorilla(skiatest::Reporter* reporter) {
+
+    double p[32];
+    for (int bit_position = 0; bit_position < 32; ++bit_position) {
+        p[bit_position] = test_single_gorilla(reporter, bit_position);
+    }
+    
+    REPORTER_ASSERT(reporter, anderson_darling_test(p));
+}
+
+void test_range(skiatest::Reporter* reporter) {
+    SkMWCRandom rand;
+    
+    // just to make sure we don't crash in this case
+    (void) rand.nextRangeU(0, 0xffffffff);
+
+    // check a case to see if it's uniform
+    int bins[256];
+    memset(bins, 0, sizeof(int)*256);
+    for (int i = 0; i < 256*10000; ++i) {
+        unsigned int u = rand.nextRangeU(17, 17+255);
+        REPORTER_ASSERT(reporter, 17 <= u && u <= 17+255);
+        bins[u - 17]++;
+    }
+
+    REPORTER_ASSERT(reporter, chi_square_test(bins, 10000));
+}
+
+static void TestRandom(skiatest::Reporter* reporter) {
+    // check uniform distributions of each byte in 32-bit word
+    test_random_byte(reporter, 0);
+    test_random_byte(reporter, 8);
+    test_random_byte(reporter, 16);
+    test_random_byte(reporter, 24);
+
+    test_random_float(reporter);
+
+    test_gorilla(reporter);
+
+    test_range(reporter);
+}
+
+#include "TestClassDef.h"
+DEFINE_TESTCLASS("Random", RandomTestClass, TestRandom)