diff --git a/pywafo/src/Wafo.egg-info/PKG-INFO b/pywafo/src/Wafo.egg-info/PKG-INFO index 702014c..5ada5a7 100644 --- a/pywafo/src/Wafo.egg-info/PKG-INFO +++ b/pywafo/src/Wafo.egg-info/PKG-INFO @@ -1,7 +1,7 @@ Metadata-Version: 1.0 Name: wafo Version: 0.1.2 -Summary: UNKNOWN +Summary: Statistical analysis and simulation of random waves and random loads Home-page: http://code.google.com/p/pywafo/ Author: WAFO-group Author-email: wafo@maths.lth.se diff --git a/pywafo/src/Wafo.egg-info/SOURCES.txt b/pywafo/src/Wafo.egg-info/SOURCES.txt index 8871d8b..987f688 100644 --- a/pywafo/src/Wafo.egg-info/SOURCES.txt +++ b/pywafo/src/Wafo.egg-info/SOURCES.txt @@ -262,14 +262,11 @@ src/wafo/spectrum/models.py src/wafo/stats/__init__.py src/wafo/stats/core.py src/wafo/stats/distributions.py -src/wafo/stats/distributions_juli2010.py src/wafo/stats/estimation.py src/wafo/stats/misc.py -src/wafo/stats/twolumps.py src/wafo/test/__init__.py src/wafo/test/test_gaussian.py src/wafo/test/test_misc.py -src/wafo/test/test_objects.py src/wafo/transform/__init__.py src/wafo/transform/core.py src/wafo/transform/models.py diff --git a/pywafo/src/wafo/kdetools.py b/pywafo/src/wafo/kdetools.py index cb4f731..1de28cc 100644 --- a/pywafo/src/wafo/kdetools.py +++ b/pywafo/src/wafo/kdetools.py @@ -18,14 +18,14 @@ from scipy.special import gamma from misc import tranproc, trangood from itertools import product -_stats_epan=(1. / 5, 3. / 5, np.inf) -_stats_biwe=(1. / 7, 5. / 7, 45. / 2), -_stats_triw=(1. / 9, 350. / 429, np.inf), -_stats_rect=(1. / 3, 1. / 2, np.inf), -_stats_tria=(1. / 6, 2. / 3, np.inf), -_stats_lapl=(2, 1. / 4, np.inf), -_stats_logi=(pi ** 2 / 3, 1. / 6, 1 / 42), -_stats_gaus=(1, 1. / (2 * sqrt(pi)), 3. / (8 * sqrt(pi))) +_stats_epan = (1. / 5, 3. / 5, np.inf) +_stats_biwe = (1. / 7, 5. / 7, 45. / 2), +_stats_triw = (1. / 9, 350. / 429, np.inf), +_stats_rect = (1. / 3, 1. / 2, np.inf), +_stats_tria = (1. / 6, 2. / 3, np.inf), +_stats_lapl = (2, 1. / 4, np.inf), +_stats_logi = (pi ** 2 / 3, 1. / 6, 1 / 42), +_stats_gaus = (1, 1. / (2 * sqrt(pi)), 3. / (8 * sqrt(pi))) @@ -100,7 +100,7 @@ class KDE(object): """ - def __init__(self, dataset, hs=None, kernel=None,L2=None,alpha=0.0): + def __init__(self, dataset, hs=None, kernel=None, L2=None, alpha=0.0): self.kernel = kernel if kernel else Kernel('gauss') self.hs = hs self.L2 = L2 @@ -121,21 +121,21 @@ class KDE(object): h = get_smoothing(self.dataset) hsiz = h.shape - if (min(hsiz)==1) or (self.d==1): - if max(hsiz)==1: - h = h*np.ones(self.d) + if (min(hsiz) == 1) or (self.d == 1): + if max(hsiz) == 1: + h = h * np.ones(self.d) else: h.shape = (self.d,) # make sure it has the correct dimension # If h negative calculate automatic values - ind, = np.where(h<=0) + ind, = np.where(h <= 0) for i in ind.tolist(): # h[i] = get_smoothing(self.dataset[i]) deth = h.prod() - self.inv_hs = linalg.diag(1.0/h) + self.inv_hs = linalg.diag(1.0 / h) else: #fully general smoothing matrix deth = linalg.det(h) - if deth<=0: + if deth <= 0: raise ValueError('bandwidth matrix h must be positive definit!') self.inv_hs = linalg.inv(h) self.hs = h @@ -188,9 +188,9 @@ class KDE(object): diff = self.dataset - points[:, i, np.newaxis] tdiff = np.dot(self.inv_hs, diff) tmp = self.kernel(tdiff) - result[i] = tmp.sum(axis=-1) + result[i] = tmp.sum(axis= -1) - result /= (self._norm_factor*self.kernel.norm_factor(d,self.n)) + result /= (self._norm_factor * self.kernel.norm_factor(d, self.n)) return result @@ -481,7 +481,7 @@ class _Kernel(object): return 1.0 def norm_kernel(self, x): X = np.atleast_2d(x) - return self._kernel(X)/self.norm_factor(*X.shape) + return self._kernel(X) / self.norm_factor(*X.shape) def kernel(self, x): return self._kernel(np.atleast_2d(x)) __call__ = kernel @@ -538,7 +538,7 @@ class _KernelRectangular(_Kernel): return np.where(np.all(np.abs(x) <= self.r, axis=0), 1, 0.0) def norm_factor(self, d=1, n=None): r = self.r - return (2*r) ** d + return (2 * r) ** d mkernel_rectangular = _KernelRectangular(stats=_stats_rect) class _KernelTriangular(_Kernel): @@ -565,7 +565,7 @@ class _KernelLaplace(_Kernel): absX = np.abs(x) return exp(-absX.sum(axis=0)) def norm_factor(self, d=1, n=None): - return 2**d + return 2 ** d mkernel_laplace = _KernelLaplace(stats=_stats_lapl) class _KernelLogistic(_Kernel): @@ -825,8 +825,8 @@ class Kernel(object): return a * linalg.sqrtm(covA) * n * (-1. / (d + 4)) - def norm_factor(self, d=1,n=None): - return self.kernel.norm_factor(n,d) + def norm_factor(self, d=1, n=None): + return self.kernel.norm_factor(n, d) def evaluate(self, X): return self.kernel(np.atleast_2d(X)) __call__ = evaluate @@ -981,7 +981,17 @@ def accum(accmap, a, func=None, size=None, fill_value=0, dtype=None): out[s] = func(vals[s]) return out - +def bitget(int_type, offset): + ''' + Returns the value of the bit at the offset position in int_type. + + Example + ------- + >>> bitget(5, np.r_[0:4]) + array([1, 0, 1, 0]) + ''' + mask = (1 << offset) + return (int_type & mask) != 0 def gridcount(data, X): ''' GRIDCOUNT D-dimensional histogram using linear binning. @@ -1016,11 +1026,13 @@ def gridcount(data, X): >>> import numpy as np >>> import wafo.kdetools as wk >>> import pylab as plb - >>> N = 500; + >>> N = 20; >>> data = np.random.rayleigh(1,N) >>> x = np.linspace(0,max(data)+1,50) >>> dx = x[1]-x[0] + >>> c = wk.gridcount(data,x) + >>> h = plb.plot(x,c,'.') # 1D histogram >>> h1 = plb.plot(x,c/dx/N) # 1D probability density plot >>> np.trapz(x,c/dx/N) @@ -1043,7 +1055,6 @@ def gridcount(data, X): if d != d1: raise ValueError('Dimension 0 of data and X do not match.') - dx = np.diff(x[:, :2], axis=1) xlo = x[:, 0] xup = x[:, -1] @@ -1061,47 +1072,46 @@ def gridcount(data, X): abs = np.abs if d == 1: x.shape = (-1,) - c = (accum(binx, (x[binx + 1] - data), size=[inc, ]) + - accum(binx, (data - x[binx]), size=[inc, ])) / w + c = (accum(binx, (x[binx + 1] - dat), size=[inc, ]) + + accum(binx, (dat - x[binx]), size=[inc, ])) / w elif d == 2: b2 = binx[1] b1 = binx[0] c_ = np.c_ stk = np.vstack - c = (accum(c_[b1, b2] , abs(np.prod(stk([X[0, b1 + 1], X[1, b2 + 1]]) - data, axis=0)), size=[inc, inc]) + - accum(c_[b1 + 1, b2] , abs(np.prod(stk([X[0, b1], X[1, b2 + 1]]) - data, axis=0)), size=[inc, inc]) + - accum(c_[b1 , b2 + 1], abs(np.prod(stk([X[0, b1 + 1], X[1, b2]]) - data, axis=0)), size=[inc, inc]) + - accum(c_[b1 + 1, b2 + 1], abs(np.prod(stk([X[0, b1], X[1, b2]]) - data, axis=0)), size=[inc, inc])) / w + c = (accum(c_[b1, b2] , abs(np.prod(stk([X[0, b1 + 1], X[1, b2 + 1]]) - dat, axis=0)), size=[inc, inc]) + + accum(c_[b1 + 1, b2] , abs(np.prod(stk([X[0, b1], X[1, b2 + 1]]) - dat, axis=0)), size=[inc, inc]) + + accum(c_[b1 , b2 + 1], abs(np.prod(stk([X[0, b1 + 1], X[1, b2]]) - dat, axis=0)), size=[inc, inc]) + + accum(c_[b1 + 1, b2 + 1], abs(np.prod(stk([X[0, b1], X[1, b2]]) - dat, axis=0)), size=[inc, inc])) / w else: # % d>2 - raise ValueError('Not implemented for d>2') + Nc = csiz.prod() - c = np.zeros((Nc, 1)) + c = np.zeros((Nc,)) - fact2 = inc * np.arange(d) - fact1 = csiz.cumprod() / inc + fact2 = np.asarray(np.reshape(inc * np.arange(d), (d, -1)), dtype=int) + fact1 = np.asarray(np.reshape(csiz.cumprod() / inc, (d, -1)), dtype=int) #fact1 = fact1(ones(n,1),:); -# for ir in xrange(2**(d-1)): -# bt0[:,:,1] = bitget(ir,1:d) -# bt0[:,:,2] = 1-bt0[:,:,1] -# for ix in range(2): -# one = mod(ix,2)+1; -# two = mod(ix+1,2)+1; -# # Convert to linear index (faster than sub2ind) -# b1 = sum((binx + bt0(ones(n,1),:,one)-1).*fact1,2)+1; #%linear index to c -# bt2 = bt0(:,:,two) + fact2; -# b2 = binx + bt2(ones(n,1),:); #% linear index to X -# -# c = c + accum(b1,abs(prod(X(b2)-data,2)),[Nc,1]); -# #c = c + accum([b1,ones(n,1)],abs(prod(X(b2)-data,2)),[Nc,1]); -# #[len,bin,val] = bincount(b1,abs(prod(X(b2)-data,2))); -# #c(bin) = c(bin)+val; -# -# #end -# #end -# c = reshape(c/w,csiz); - #end - if d == 2: #% make sure c is stored in the same way as meshgrid + bt0 = [0, 0] + X1 = X.ravel() + for ir in xrange(2 ** (d - 1)): + bt0[0] = np.reshape(bitget(ir, np.arange(d)), (d, -1)) + bt0[1] = 1 - bt0[0] + for ix in xrange(2): + one = np.mod(ix, 2) + two = np.mod(ix + 1, 2) + # Convert to linear index + b1 = np.sum((binx + bt0[one]) * fact1, axis=0) #linear index to c + bt2 = bt0[two] + fact2 + b2 = binx + bt2 # linear index to X + c += accum(b1, abs(np.prod(X1[b2] - dat, axis=0)), size=(Nc,)) + + c = np.reshape(c / w, csiz) + # TODO: check that the flipping of axis is correct + T = range(d); T[-2],T[-1] = T[-1], T[-2] + c = c.transpose(*T) + + if d == 2: # make sure c is stored in the same way as meshgrid c = c.T elif d == 3: c = c.transpose(1, 0, 2) @@ -1119,7 +1129,7 @@ def test_kde(): f = kde(x) #plb.hist(data.ravel()) - plb.plot(x,f) + plb.plot(x, f) plb.show() def test_gridcount(): @@ -1129,7 +1139,8 @@ def test_gridcount(): N = 500; data = np.random.rayleigh(1, size=(2, N)) x = np.linspace(0, max(data.ravel()) + 1, 10) - X = np.vstack((x, x)) + X = np.vstack((x, x)) + dx = x[1] - x[0] c = wk.gridcount(data, X) h = plb.contourf(x, x, c) @@ -1147,4 +1158,4 @@ def main(): if __name__ == '__main__': #main() #test_gridcount() - test_kde() \ No newline at end of file + test_kde() diff --git a/pywafo/src/wafo/stats/distributions.py b/pywafo/src/wafo/stats/distributions.py index f670df1..f7f17f2 100644 --- a/pywafo/src/wafo/stats/distributions.py +++ b/pywafo/src/wafo/stats/distributions.py @@ -216,6 +216,22 @@ Check accuracy of cdf and ppf Random number generation >>> R = %(name)s.rvs(%(shapes)s, size=100) + +Compare ML and MPS method +>>> phat = %(name)s.fit2(R, method='ml'); +>>> phat.plotfitsummary(); plt.figure(plt.gcf().number+1) +>>> phat2 = %(name)s.fit2(R, method='mps') +>>> phat2.plotfitsummary(); plt.figure(plt.gcf().number+1) + +Fix loc=0 and estimate shapes and scale +>>> phat3 = %(name)s.fit2(R, scale=1, floc=0, method='mps') +>>> phat3.plotfitsummary(); plt.figure(plt.gcf().number+1) + +Accurate confidence interval with profile loglikelihood +>>> lp = phat3.profile() +>>> lp.plot() +>>> pci = lp.get_bounds() + """ _doc_default = ''.join([_doc_default_longsummary, diff --git a/pywafo/src/wafo/stats/estimation.py b/pywafo/src/wafo/stats/estimation.py index 3a67733..7172183 100644 --- a/pywafo/src/wafo/stats/estimation.py +++ b/pywafo/src/wafo/stats/estimation.py @@ -713,7 +713,7 @@ class FitDistribution(rv_frozen): '''Compute covariance ''' somefixed = (self.par_fix != None) and any(isfinite(self.par_fix)) - H1 = numpy.asmatrix(self.dist.hessian_nnlf(self.par, self.data)) + #H1 = numpy.asmatrix(self.dist.hessian_nnlf(self.par, self.data)) H = numpy.asmatrix(self.dist.hessian_nlogps(self.par, self.data)) self.H = H try: diff --git a/pywafo/src/wafo/stats/tests/test_estimation.py b/pywafo/src/wafo/stats/tests/test_estimation.py index 5450cf2..107c5b5 100644 --- a/pywafo/src/wafo/stats/tests/test_estimation.py +++ b/pywafo/src/wafo/stats/tests/test_estimation.py @@ -7,7 +7,7 @@ Created on 19. nov. 2010 import wafo.stats as ws from wafo.stats.estimation import Profile, FitDistribution from numpy import log, array -def test_profile(): +def test_fit_and_profile(): ''' # MLE import wafo.stats as ws