def test_tt(self):
        true_sampler = theano.function([self.coin_weight], self.coin)

        sample, ll, updates = mh_sample(self.s_rng, [self.coin_weight])
        sampler = theano.function([self.coin], sample, updates=updates)

        for i in range(100):
            print sampler(true_sampler(0.9))
Beispiel #2
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    def test_tt(self):
        true_sampler = theano.function([self.coin_weight], self.coin)

        sample, ll, updates = mh_sample(self.s_rng, [self.coin_weight])
        sampler = theano.function([self.coin], sample, updates=updates)

        for i in range(100):
            print sampler(true_sampler(0.9))
 def test_mh_sample(self):
     sample, ll, updates = mh_sample(self.s_rng, [self.M, self.V], observations={self.X: self.X_data}, lag = 100)
     sampler = theano.function([], sample, updates=updates)
     
     data = []
     for i in range(100):
         print i
         data.append(sampler())
     
     pylab.subplot(211)
     pylab.hist(numpy.asarray(data)[:,0])
     pylab.subplot(212)
     pylab.hist(numpy.asarray(data)[:,1])
     pylab.show()
Beispiel #4
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    def test_mh_sample(self):
        sample, ll, updates = mh_sample(self.s_rng, [self.M, self.V],
                                        observations={self.X: self.X_data},
                                        lag=100)
        sampler = theano.function([], sample, updates=updates)

        data = []
        for i in range(100):
            print i
            data.append(sampler())

        pylab.subplot(211)
        pylab.hist(numpy.asarray(data)[:, 0])
        pylab.subplot(212)
        pylab.hist(numpy.asarray(data)[:, 1])
        pylab.show()
                            [0,0,1,0],
                            [0,0,1,0],
                            [0,0,1,0]], dtype=theano.config.floatX)

document_3 = numpy.asarray([[1,0,0,0],
                            [0,0,0,1],
                            [0,1,0,0],
                            [0,1,0,0],
                            [0,0,1,0],
                            [0,1,0,0],
                            [0,0,0,1],
                            [1,0,0,0],
                            [0,0,1,0],
                            [0,0,1,0]], dtype=theano.config.floatX)

# Map documents to RVs
givens = {get_words(1, 10): document_1,
            get_words(2, 10): document_2,
            get_words(3, 10): document_3}

# Build sampler
sample, ll, updates = mh_sample(s_rng, [doc_mixture(1), doc_mixture(2), doc_mixture(3), topic_mixture(0), topic_mixture(1)])
sampler = theano.function([], sample, updates=updates, givens=givens, allow_input_downcast=True)

# Run sampling
for i in range(10000):
    d = sampler()            

    if i % 1000 == 0:
        print d
    [[1, 0, 0, 0], [0, 0, 0, 1], [0, 1, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0],
     [0, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 0], [0, 0, 1, 0]],
    dtype=theano.config.floatX)

# Map documents to RVs
givens = {
    get_words(1, 10): document_1,
    get_words(2, 10): document_2,
    get_words(3, 10): document_3
}

# Build sampler
sample, ll, updates = mh_sample(s_rng, [
    doc_mixture(1),
    doc_mixture(2),
    doc_mixture(3),
    topic_mixture(0),
    topic_mixture(1)
])
sampler = theano.function([],
                          sample,
                          updates=updates,
                          givens=givens,
                          allow_input_downcast=True)

# Run sampling
for i in range(10000):
    d = sampler()

    if i % 1000 == 0:
        print d