add some tests

theano/sandbox/tests/test_weighted_select.py

+import numpy
+from theano import config, function, tensor
+from theano.sandbox import multinomial
+
+def test_select_distinct():
+    p = tensor.fmatrix()
+    u = tensor.fvector()
+    n = tensor.iscalar()
+    m = multinomial.WeightedSelectionFromUniform('auto')(p, u, n)
+    
+    f = function([p, u, n], m, allow_input_downcast=True)
+    
+    n_elements = 1000
+    numpy.random.seed(12345)
+    for i in [5, 10, 50, 100, 500]:
+        uni = numpy.random.rand(i).astype(config.floatX)
+        pvals = numpy.random.randint(1,100,(1,n_elements)).astype(config.floatX)
+        pvals /= pvals.sum(1)
+        res = f(pvals, uni, i)
+        res = numpy.squeeze(res)
+        assert len(res) == i
+        assert numpy.all(numpy.in1d(numpy.unique(res), res)), res
+
+def test_select_all():
+    p = tensor.fmatrix()
+    u = tensor.fvector()
+    n = tensor.iscalar()
+    m = multinomial.WeightedSelectionFromUniform('auto')(p, u, n)
+    
+    f = function([p, u, n], m, allow_input_downcast=True)
+    
+    n_elements = 1000
+    numpy.random.seed(12345)
+    for _ in range(100):
+        uni = numpy.random.rand(n_elements).astype(config.floatX)
+        pvals = numpy.random.randint(1,100,(1,n_elements)).astype(config.floatX)
+        pvals /= pvals.sum(1)
+        res = f(pvals, uni, n_elements)
+        res = numpy.squeeze(res)
+        assert len(res) == n_elements
+        assert numpy.all(numpy.in1d(numpy.unique(res), res)), res
+
+        
+def test_select_proportional_to_weight():
+    p = tensor.fmatrix()
+    u = tensor.fvector()
+    n = tensor.iscalar()
+    m = multinomial.WeightedSelectionFromUniform('auto')(p, u, n)
+    
+    f = function([p, u, n], m, allow_input_downcast=True)
+
+    n_elements = 100
+    n_selected = 10
+    mean_rtol = 0.04
+    numpy.random.seed(12345)
+    pvals = numpy.random.randint(1,100,(1,n_elements)).astype(config.floatX)
+    pvals /= pvals.sum(1)
+    avg_pvals = numpy.zeros((n_elements,))
+    
+    for rep in range(1000):
+        uni = numpy.random.rand(n_selected).astype(config.floatX)
+        res = f(pvals, uni, n_selected)
+        res = numpy.squeeze(res)
+        # print res
+        avg_pvals[res] += 1
+    avg_pvals /= avg_pvals.sum()
+    
+    print avg_pvals
+    print numpy.squeeze(pvals)
+    assert numpy.mean(abs(avg_pvals - pvals)) < mean_rtol