Revert "Remove duplicated test"

theano/sandbox/tests/test_rng_mrg.py

@@ -501,6 +501,63 @@ def test_normal_truncation():
                   prefix='mrg ', allow_01=True, inputs=input,
                   mean_rtol=rtol, std_tol=std_tol)
 
+        sys.stdout.flush()
+
+
+@attr('slow')
+def test_truncated_normal():
+    # just a copy of test_normal0 for truncated normal
+    steps = 50
+    std = 2.
+
+    if (config.mode in ['DEBUG_MODE', 'DebugMode', 'FAST_COMPILE'] or
+            config.mode == 'Mode' and config.linker in ['py']):
+        sample_size = (25, 30)
+        default_rtol = .02
+    else:
+        sample_size = (999, 50)
+        default_rtol = .01
+    sample_size_odd = (sample_size[0], sample_size[1] - 1)
+    x = tensor.matrix()
+
+    test_cases = [
+        (sample_size, sample_size, [], [], -5., default_rtol, default_rtol),
+        (x.shape, sample_size, [x],
+         [np.zeros(sample_size, dtype=config.floatX)],
+         -5., default_rtol, default_rtol),
+        # test odd value
+        (x.shape, sample_size_odd, [x],
+         [np.zeros(sample_size_odd, dtype=config.floatX)],
+         -5., default_rtol, default_rtol),
+        (sample_size, sample_size, [], [],
+         np.arange(np.prod(sample_size),
+                   dtype='float32').reshape(sample_size),
+         10. * std / np.sqrt(steps), default_rtol),
+        # test empty size (scalar)
+        ((), (), [], [], -5., default_rtol, 0.02),
+        # test with few samples at the same time
+        ((1,), (1,), [], [], -5., default_rtol, 0.02),
+        ((3,), (3,), [], [], -5., default_rtol, 0.02),
+    ]
+
+    for size, const_size, var_input, input, avg, rtol, std_tol in test_cases:
+        R = MRG_RandomStreams(234)
+        # Note: we specify `nstreams` to avoid a warning.
+        n = R.truncated_normal(size=size, avg=avg, std=std,
+                               nstreams=rng_mrg.guess_n_streams(size, warn=False))
+        f = theano.function(var_input, n)
+
+        # Increase the number of steps if size implies only a few samples
+        if np.prod(const_size) < 10:
+            steps_ = steps * 60
+        else:
+            steps_ = steps
+        basictest(f, steps_, const_size, target_avg=avg, target_std=std,
+                  prefix='mrg ', allow_01=True, inputs=input,
+                  mean_rtol=rtol, std_tol=std_tol)
+
+        sys.stdout.flush()
+
 
 def basic_multinomialtest(f, steps, sample_size, target_pvals, n_samples,
                           prefix="", mean_rtol=0.04):