From 7657b53b3d8dc8dcee4656d5e8b392eb8e5ccf6d Mon Sep 17 00:00:00 2001 From: Per A Brodtkorb Date: Mon, 2 Jan 2017 16:06:22 +0100 Subject: [PATCH] Removed obsolete code and added more tests. --- wafo/kdetools/demo.py | 13 ++++++++--- wafo/kdetools/kdetools.py | 21 ----------------- wafo/kdetools/tests/test_kdetools.py | 34 +++++++++++++++++++++------- 3 files changed, 36 insertions(+), 32 deletions(-) diff --git a/wafo/kdetools/demo.py b/wafo/kdetools/demo.py index 29780b4..aeaf6bf 100644 --- a/wafo/kdetools/demo.py +++ b/wafo/kdetools/demo.py @@ -32,9 +32,9 @@ def kde_demo1(): data = np.random.normal(loc=0, scale=1.0, size=7) kernel = Kernel('gauss') hs = kernel.hns(data) - hVec = [hs / 2, hs, 2 * hs] + h_vec = [hs / 2, hs, 2 * hs] - for ix, h in enumerate(hVec): + for ix, h in enumerate(h_vec): plt.figure(ix) kde = KDE(data, hs=h, kernel=kernel) f2 = kde(x, output='plot', title='h_s = {0:2.2f}'.format(float(h)), @@ -176,7 +176,8 @@ def kde_demo5(N=500): def kreg_demo1(hs=None, fast=False, fun='hisj'): - """""" + """Compare KRegression to KernelReg from statsmodels.nonparametric + """ N = 100 # ei = np.random.normal(loc=0, scale=0.075, size=(N,)) ei = np.array([ @@ -236,6 +237,9 @@ def kreg_demo1(hs=None, fast=False, fun='hisj'): def _get_data(n=100, symmetric=False, loc1=1.1, scale1=0.6, scale2=1.0): + """ + Return test data for binomial regression demo. + """ st = scipy.stats dist = st.norm @@ -262,6 +266,9 @@ def _get_data(n=100, symmetric=False, loc1=1.1, scale1=0.6, scale2=1.0): def check_bkregression(): + """ + Check binomial regression + """ plt.ion() k = 0 for _i, n in enumerate([50, 100, 300, 600]): diff --git a/wafo/kdetools/kdetools.py b/wafo/kdetools/kdetools.py index c7e2268..3e8f251 100644 --- a/wafo/kdetools/kdetools.py +++ b/wafo/kdetools/kdetools.py @@ -412,27 +412,6 @@ class TKDE(_KDE): Check the KDE for spurious spikes''') return pdf - def eval_grid_fast2(self, *args, **kwds): - """Evaluate the estimated pdf on a grid. - - Parameters - ---------- - arg_0,arg_1,... arg_d-1 : vectors - Alternatively, if no vectors is passed in then - arg_i = gauss2dat(linspace(dat2gauss(self.xmin[i]), - dat2gauss(self.xmax[i]), self.inc)) - output : string optional - 'value' if value output - 'data' if object output - - Returns - ------- - values : array-like - The values evaluated at meshgrid(*args). - - """ - return self.eval_grid_fun(self._eval_grid_fast, *args, **kwds) - def _interpolate(self, points, f, *args, **kwds): ipoints = meshgrid(*args) # if self.d > 1 else args for i in range(self.d): diff --git a/wafo/kdetools/tests/test_kdetools.py b/wafo/kdetools/tests/test_kdetools.py index b3fa582..195240d 100644 --- a/wafo/kdetools/tests/test_kdetools.py +++ b/wafo/kdetools/tests/test_kdetools.py @@ -103,15 +103,12 @@ class TestKde(unittest.TestCase): assert_allclose(f, [1.03982714, 0.45839018, 0.39514782, 0.32860602, 0.26433318, 0.20717946, 0.15907684, 0.1201074, 0.08941027, 0.06574882]) - + f = kde.eval_grid(x) + assert_allclose(f, [1.03982714, 0.45839018, 0.39514782, 0.32860602, + 0.26433318, 0.20717946, 0.15907684, 0.1201074, + 0.08941027, 0.06574882]) assert_allclose(np.trapz(f, x), 0.94787730659349068) f = kde.eval_grid_fast(x) - assert_allclose(f, [1.0401892415290148, 0.45838973393693677, - 0.39514689240671547, 0.32860531818532457, - 0.2643330110605783, 0.20717975528556506, - 0.15907696844388747, 0.12010770443337843, - 0.08941129458260941, 0.06574899139165799]) - f = kde.eval_grid_fast2(x) assert_allclose(f, [1.0401892415290148, 0.45838973393693677, 0.39514689240671547, 0.32860531818532457, 0.2643330110605783, 0.20717975528556506, @@ -340,7 +337,7 @@ class TestRegression(unittest.TestCase): bkreg = wk.BKRegression(x, y, a=0.05, b=0.05) fbest = bkreg.prb_search_best(hsfun='hste', alpha=0.05, color='g') - print(fbest.data[::10].tolist()) + # print(fbest.data[::10].tolist()) assert_allclose(fbest.data[::10], [1.80899736e-15, 0, 6.48351162e-16, 6.61404311e-15, @@ -355,6 +352,27 @@ class TestRegression(unittest.TestCase): 2.68633448e-04, 1.68974054e-05, 5.73040143e-07, 1.11994760e-08, 1.36708818e-10, 1.09965904e-12, 5.43806309e-15, 0.0, 0, 0], atol=1e-10) + bkreg = wk.BKRegression(x, y, method='wilson') + fbest = bkreg.prb_search_best(hsfun='hste', alpha=0.05, color='g') + assert_allclose(fbest.data[::10], + [3.2321397702105376e-15, 4.745626420805898e-17, + 6.406118940191104e-16, 5.648884668051452e-16, + 3.499875381296387e-16, 1.0090442883241678e-13, + 4.264723863193633e-11, 9.29288388831705e-09, + 9.610074789043923e-07, 4.086642453634508e-05, + 0.0008305202502773989, 0.00909121197102206, + 0.05490768364395013, 0.1876637145781381, + 0.4483015169104682, 0.8666709816557657, + 0.9916656713022183, 0.9996648903706271, + 0.999990921956741, 0.9999909219567404, + 0.999664890370625, 0.9916656713022127, + 0.8666709816557588, 0.4483015169104501, + 0.18766371457812697, 0.054907683643947366, + 0.009091211971022042, 0.0008305202502770367, + 4.086642453593762e-05, 9.610074786590158e-07, + 9.292883469982049e-09, 4.264660017463372e-11, + 1.005284921271869e-13, -0.0, -0.0, -0.0, -0.0, -0.0], + atol=1e-10) if __name__ == "__main__": # import sys;sys.argv = ['', 'Test.testName']