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%load_ext autoreload
%autoreload 2
%matplotlib inline
#%config InlineBackend.figure_format = 'svg'
#%config InlineBackend.figure_format = 'pdf'
import freqopttest.util as util
import freqopttest.data as data
import freqopttest.ex.exglobal as exglo
import freqopttest.kernel as kernel
import freqopttest.tst as tst
import freqopttest.glo as glo
import freqopttest.plot as plot
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats
import sys


    

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# sample source 
m = 2000
dim = 20
seed = 9
n = m
#ss = data.SSGaussMeanDiff(dim, my=1.0)
ss = data.SSGaussVarDiff(dim)
#ss = data.SSSameGauss(dim)
#ss = data.SSBlobs()
dim = ss.dim()
tst_data = ss.sample(m, seed=seed)
tr, te = tst_data.split_tr_te(tr_proportion=0.5, seed=10)
#te = tst_data


    

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J = 5
alpha = 0.01

T = tst.MeanEmbeddingTest.init_locs_2randn(tr, J, seed=seed+1)
#T = np.random.randn(J, dim)

med = util.meddistance(tr.stack_xy(), 500)
list_gwidth = np.hstack( ( (med**2) *(2.0**np.linspace(-5, 5, 40) ) ) )
list_gwidth.sort()
besti, powers = tst.MeanEmbeddingTest.grid_search_gwidth(tr, T, list_gwidth, alpha)


    

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best_width = list_gwidth[besti]
met_grid = tst.MeanEmbeddingTest(T, best_width, alpha)
met_grid.perform_test(te)


    

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n = 100
# m draws
m = 1000
J =5
# noncentrality parameter
nc = 10
ncx2_sam = np.array( [stats.ncx2.rvs(J, nc) for i in range(m)] )


    

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