// test set 5
func Test_stats_5(t *testing.T) {
data := FloatSlice{16.0, 99.0, 26.0, 85.0, 76.0, 50.0, 46.0, 11.0, 79.0,
97.0, 24.0, 20.0, 100.0, 68.0, 22.0, 15.0, 5.0, 89.0, 45.0, 2.0}
max := data.Max(1)
if !util.FloatEqual(max, 100.0) {
t.Error("Test 5: Failed to compute data max.")
}
min := data.Min(1)
if !util.FloatEqual(min, 2.0) {
t.Error("Test 5: Failed to compute data min.")
}
min1, max1 := data.MinMax(1)
if !util.FloatEqual(min, min1) || !util.FloatEqual(max, max1) {
t.Error("Test 5: Failed to compute data minmax.")
}
maxInd := data.MaxIndex(1)
if maxInd != 12 {
t.Error("Test 5: Failed to compute data max index.")
}
minInd := data.MinIndex(1)
if minInd != 19 {
t.Error("Test 5: Failed to compute min index.")
}
minInd1, maxInd1 := data.MinMaxIndex(1)
if (minInd != minInd1) || (maxInd != maxInd1) {
t.Error("Test 5: Failed to compute data minmax index.")
}
}
// test set 3
func Test_stats_3(t *testing.T) {
data1 := FloatSlice{16.0, 99.0, 26.0, 85.0, 76.0, 50.0, 46.0, 11.0, 79.0,
97.0, 24.0, 20.0, 100.0, 68.0, 22.0, 15.0, 5.0, 89.0, 45.0, 2.0}
data2 := FloatSlice{39.0, 10.0, 34.0, 29.0, 82.0, 54.0, 30.0, 65.0, 56.0,
55.0, 20.0, 52.0, 96.0, 95.0, 23.0, 51.0, 27.0, 59.0, 31.0, 99.0}
mean1 := data1.Mean(1)
mean2 := data2.Mean(1)
cov := data1.Covariance(1, data2, 1)
if !util.FloatEqual(cov, 130.93421052631578) {
t.Error("Test 3: Failed to compute covariance.")
}
cov_m := data1.Covariance_m(1, data2, 1, mean1, mean2)
if !util.FloatEqual(cov_m, cov) {
t.Error("Test 3: Failed to compute covariance with mean1 and mean2.")
}
corr := data1.Correlation(1, data2, 1)
if !util.FloatEqual(corr, 0.14269187753186113) {
t.Error("Test 3: Failed to compute Pearson correlation.")
}
spear := data1.Spearman(1, data2, 1)
if !util.FloatEqual(spear, 0.091729323308270688) {
t.Error("Test 3: Failed to compute Pearson correlation.")
}
}
// test set 2
// XXX: This a are silly tests that only check that the function work
// but not that they work correctly. Not sure how to check correctness.
func Test_randist_2(t *testing.T) {
// test gaussian tail distributions
rng_type := Ranlxd2
rng_state := Rng_alloc(rng_type)
// gaussian
tail := GaussianTailSlice(rng_state, 0, 1, numSamples)
if len(tail) != int(numSamples) {
t.Error("randist: error computing gaussian tail distribution")
}
// unit gaussian
utail := UgaussianTailSlice(rng_state, 0, numSamples)
if len(utail) != int(numSamples) {
t.Error("randist: error computing unit gaussian tail distribution")
}
// pdfs
g_pdf := GaussianTailPdf(1, 0, 1)
ug_pdf := UgaussianTailPdf(1, 0)
if !util.FloatEqual(g_pdf, ug_pdf) {
t.Error("randist: missmatch in computing gaussian tail pdfs",
g_pdf, ug_pdf)
}
}
// test set 2
func Test_stats_2(t *testing.T) {
norm_data := FloatSlice{1.33830225764885e-04, 3.52595682367445e-04,
8.72682695045760e-04, 2.02904805729977e-03, 4.43184841193801e-03,
9.09356250159105e-03, 1.75283004935685e-02, 3.17396518356674e-02,
5.39909665131881e-02, 8.62773188265115e-02, 1.29517595665892e-01,
1.82649085389022e-01, 2.41970724519143e-01, 3.01137432154804e-01,
3.52065326764300e-01, 3.86668116802849e-01, 3.98942280401433e-01,
3.86668116802849e-01, 3.52065326764300e-01, 3.01137432154804e-01,
2.41970724519143e-01, 1.82649085389022e-01, 1.29517595665892e-01,
8.62773188265115e-02, 5.39909665131881e-02, 3.17396518356674e-02,
1.75283004935685e-02, 9.09356250159105e-03, 4.43184841193801e-03,
2.02904805729977e-03, 8.72682695045760e-04, 3.52595682367445e-04,
1.33830225764885e-04}
mean := norm_data.Mean(1)
if !util.FloatEqual(mean, 0.12120783192361629654) {
t.Error("Test 2: Failed to compute mean.")
}
sd := norm_data.Sd(1)
if !util.FloatEqual(sd, 0.1418146888138111239) {
t.Error("Test 2: Failed to compute standard deviation.")
}
skew := norm_data.Skew(1)
if !util.FloatEqual(skew, 0.79446149715833858096) {
t.Error("Test 2: Failed to compute skew.")
}
skew_m_sd := norm_data.Skew_m_sd(1, mean, sd)
if !util.FloatEqual(skew_m_sd, skew) {
t.Error("Test 2: Failed to compute skew with mean and stddev.")
}
kurt := norm_data.Kurtosis(1)
if !util.FloatEqual(kurt, -0.98591331325429809596) {
t.Error("Test 2: Failed to compute kurtosis.")
}
kurt_m_sd := norm_data.Kurtosis_m_sd(1, mean, sd)
if !util.FloatEqual(kurt_m_sd, kurt) {
t.Error("Test 2: Failed to compute kurtosis with mean and stddev.")
}
// XXX: The target values for l1cor have been computed via GSL and
// were not tested by a third party tool
l1corr := norm_data.Lag1_autocorrelation(1)
if !util.FloatEqual(l1corr, 0.9500395402358169) {
t.Error("Test 2: Failed to compute the lag1 autocorrelation.", l1corr)
}
l1corr_m_sd := norm_data.Lag1_autocorrelation_m_sd(1, mean)
if !util.FloatEqual(l1corr_m_sd, l1corr) {
t.Error("Test 2: Failed to compute lag1 autocorrelation with mean and stddev.", l1corr_m_sd)
}
}
// test set 6
func Test_stats_6(t *testing.T) {
data := FloatSlice{1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0}
median := data.MedianFromSortedData(1)
if !util.FloatEqual(median, 5.5) {
t.Error("Test 6: Failed to compute median value.")
}
quantile1 := data.QuantileFromSortedData(1, 0.15)
if !util.FloatEqual(quantile1, 2.35) {
t.Error("Test 6: Failed to compute quantile1 value.")
}
quantile2 := data.QuantileFromSortedData(1, 0.92)
if !util.FloatEqual(quantile2, 9.28) {
t.Error("Test 6: Failed to compute quantile2 value.")
}
}
// test set 1
func Test_stats_1(t *testing.T) {
data := FloatSlice{1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0}
mean := data.Mean(1)
if !util.FloatEqual(mean, 5.5) {
t.Error("Test 1: Failed to compute mean.")
}
sigma2 := data.Variance(1)
if !util.FloatEqual(sigma2, 9.166666666666666) {
t.Error("Test 1: Failed to compute variance.")
}
sigma2_m := data.Variance_m(1, mean)
if !util.FloatEqual(sigma2_m, 9.166666666666666) {
t.Error("Test 1: Failed to compute variance.")
}
sigma := data.Sd(1)
if !util.FloatEqual(sigma, math.Sqrt(sigma2)) {
t.Error("Test 1: Failed to compute standard deviation.")
}
sigma_m := data.Sd_m(1, mean)
if !util.FloatEqual(sigma_m, math.Sqrt(sigma2)) {
t.Error("Test 1: Failed to compute standard deviation with mean.")
}
tss_comp := 0.0
for _, v := range data {
tss_comp += (v - mean) * (v - mean)
}
tss := data.Tss(1)
if !util.FloatEqual(tss, tss_comp) {
t.Error("Test 1: Failed to compute total sum of squares.")
}
tss_m := data.Tss_m(1, mean)
if !util.FloatEqual(tss, tss_m) {
t.Error("Test 1: Failed to compute total sum of squares with mean.")
}
sigma2_fixed := data.Variance_with_fixed_mean(1, mean)
if !util.FloatEqual(sigma2_fixed, 8.25) {
t.Error("Test 1: Failed to compute variance with fixed mean.")
}
sigma_fixed := data.Sd_with_fixed_mean(1, mean)
if !util.FloatEqual(sigma_fixed, math.Sqrt(sigma2_fixed)) {
t.Error("Test 1: Failed to compute standard deviation with fixed mean.")
}
absdev := data.Absdev(1)
if !util.FloatEqual(absdev, 2.5) {
t.Error("Test 1: Failed to compute absolute deviation.")
}
absdev_m := data.Absdev_m(1, mean)
if !util.FloatEqual(absdev_m, absdev) {
t.Error("Test 1: Failed to compute absolute deviation with mean.")
}
}
// test set 4
func Test_stats_4(t *testing.T) {
data := FloatSlice{16.0, 99.0, 26.0, 85.0, 76.0, 50.0, 46.0, 11.0, 79.0,
97.0, 24.0, 20.0, 100.0, 68.0, 22.0, 15.0, 5.0, 89.0, 45.0, 2.0}
weights := FloatSlice{0.1, 0.2, 0.3, 0.4, 0.1, 0.2, 0.2, 0.1, 0.8,
0.8, 0.1, 0.2, 0.1, 0.1, 0.2, 0.3, 0.3, 0.3, 0.2, 0.1}
wmean := data.Wmean(1, weights, 1)
if !util.FloatEqual(wmean, 59.098039215686278) {
t.Error("Test 4: Failed to compute weighted mean.")
}
// XXX: All values below in this test set were computed via GSL
// and not confirmened to be correct by a third party application
wvar := data.Wvariance(1, weights, 1)
if !util.FloatEqual(wvar, 1248.9347280334725) {
t.Error("Test 4: Failed to compute weighted variance.")
}
wvar_m := data.Wvariance_m(1, weights, 1, wmean)
if !util.FloatEqual(wvar_m, wvar) {
t.Error("Test 4: Failed to compute weighted variance with mean.")
}
wsd := data.Wsd(1, weights, 1)
if !util.FloatEqual(wsd, 35.34027062762073) {
t.Error("Test 4: Failed to compute weighted stddev.")
}
wsd_m := data.Wsd_m(1, weights, 1, wmean)
if !util.FloatEqual(wsd_m, wsd) {
t.Error("Test 4: Failed to compute weighted stdev with mean.")
}
wvariance_fixed_m := data.Wvariance_with_fixed_mean(1, weights, 1, wmean)
if !util.FloatEqual(wvariance_fixed_m, 1147.6178392925797) {
t.Error("Test 4: Failed to compute variance with fixed mean.")
}
wsd_fixed_m := data.Wsd_with_fixed_mean(1, weights, 1, wmean)
if !util.FloatEqual(wsd_fixed_m, math.Sqrt(wvariance_fixed_m)) {
t.Error("Test 4: Failed to compute stddev with fixed mean.")
}
wtss := data.Wtss(1, weights, 1)
if !util.FloatEqual(wtss, 5852.850980392157) {
t.Error("Test 4: Failed to compute weighted sum of squares.")
}
wtss_m := data.Wtss_m(1, weights, 1, wmean)
if !util.FloatEqual(wtss_m, wtss) {
t.Error("Test 4: Failed to compute weighted sum of squares with mean.")
}
wabsdev := data.Wabsdev(1, weights, 1)
if !util.FloatEqual(wabsdev, 31.46097654748174) {
t.Error("Test 4: Failed to compute weighted absolute deviation.")
}
wabsdev_m := data.Wabsdev_m(1, weights, 1, wmean)
if !util.FloatEqual(wabsdev_m, wabsdev) {
t.Error("Test 4: Failed to compute weighted absolute deviation with mean.")
}
wskew := data.Wskew(1, weights, 1)
if !util.FloatEqual(wskew, -0.28679295109648656) {
t.Error("Test 4: Failed to compute weighted skew.")
}
wskew_m_sd := data.Wskew_m_sd(1, weights, 1, wmean, wsd)
if !util.FloatEqual(wskew_m_sd, wskew) {
t.Error("Test 4: Failed to compute weighted skew with mean and stddev.")
}
wkurtosis := data.Wkurtosis(1, weights, 1)
if !util.FloatEqual(wkurtosis, -1.7414231965732037) {
t.Error("Test 4: Failed to compute weighted kurtosis.")
}
wkurtosis_m_sd := data.Wkurtosis_m_sd(1, weights, 1, wmean, wsd)
if !util.FloatEqual(wkurtosis_m_sd, wkurtosis) {
t.Error("Test 4: Failed to compute weighted kurtosis with mean and stddev.")
}
}
// test set 1
func Test_cdf_1(t *testing.T) {
// gaussian
if !util.FloatEqual(GaussianP(0, 1), 0.5) {
t.Error("cdf: error computing P(0).")
}
if !util.FloatEqual(GaussianP(0, 10), 0.5) {
t.Error("cdf: error computing P(0).")
}
if !util.FloatEqual(GaussianQ(0, 1), 0.5) {
t.Error("cdf: error computing P(0).")
}
if !util.FloatEqual(GaussianQ(0, 10), 0.5) {
t.Error("cdf: error computing P(0).")
}
if !util.FloatEqual(GaussianPinv(0.5, 1), 0) {
t.Error("cdf: error computing Pinv(0.5).")
}
if !util.FloatEqual(GaussianPinv(0.5, 10), 0) {
t.Error("cdf: error computing Pinv(0.5).")
}
if !util.FloatEqual(GaussianQinv(0.5, 1), 0) {
t.Error("cdf: error computing Qinv(0.5).")
}
if !util.FloatEqual(GaussianQinv(0.5, 10), 0) {
t.Error("cdf: error computing Qinv(0.5).")
}
// unit gaussian
if !util.FloatEqual(UgaussianP(0), 0.5) {
t.Error("cdf: error computing P(0).")
}
if !util.FloatEqual(UgaussianQ(0), 0.5) {
t.Error("cdf: error computing P(0).")
}
if !util.FloatEqual(UgaussianPinv(0.5), 0) {
t.Error("cdf: error computing Pinv(0.5).")
}
if !util.FloatEqual(UgaussianQinv(0.5), 0) {
t.Error("cdf: error computing Qinv(0.5).")
}
}
