rm dupplicated code

Author: juanitorduz

test/test_distributions.py

@@ -4640,109 +4640,6 @@ def test_interval_censored_validate_sample(
         censored_dist.log_prob(value)  # Should not raise
 
 
-def test_uniform_log_prob_outside_support():
-    d = dist.Uniform(0, 1)
-    assert_allclose(d.log_prob(-0.5), -jnp.inf)
-    assert_allclose(d.log_prob(1.5), -jnp.inf)
-
-
-@pytest.mark.parametrize(
-    "low, high", [(0.0, 1.0), (-2.0, 3.0), (1.0, 5.0), (-5.0, -1.0)]
-)
-def test_uniform_log_prob_boundaries(low, high):
-    """Test that boundary values are handled correctly."""
-    d = dist.Uniform(low, high)
-    expected_log_prob = -jnp.log(high - low)
-
-    # Value at lower bound (included): should have finite log prob
-    assert_allclose(d.log_prob(low), expected_log_prob)
-
-    # Value just above lower bound: should have finite log prob
-    assert_allclose(d.log_prob(low + 1e-10), expected_log_prob)
-
-    # Value at upper bound (excluded): should be -inf
-    assert_allclose(d.log_prob(high), -jnp.inf)
-
-    # Value just below upper bound: should have finite log prob
-    assert_allclose(d.log_prob(high - 1e-10), expected_log_prob)
-
-    # Value inside support: should have finite log prob
-    mid = (low + high) / 2.0
-    assert_allclose(d.log_prob(mid), expected_log_prob)
-
-    # Value below lower bound: should be -inf
-    assert_allclose(d.log_prob(low - 1.0), -jnp.inf)
-
-    # Value above upper bound: should be -inf
-    assert_allclose(d.log_prob(high + 1.0), -jnp.inf)
-
-
-@pytest.mark.parametrize("batch_shape", [(), (3,), (2, 3), (4, 2, 3)])
-def test_uniform_log_prob_broadcasting(batch_shape):
-    """Test broadcasting with different batch shapes."""
-    if batch_shape == ():
-        low = 0.0
-        high = 1.0
-    else:
-        low = jnp.linspace(0.0, 1.0, np.prod(batch_shape)).reshape(batch_shape)
-        high = jnp.linspace(1.0, 2.0, np.prod(batch_shape)).reshape(batch_shape)
-
-    d = dist.Uniform(low, high)
-
-    # Test with scalar value
-    value = 0.5
-    log_probs = d.log_prob(value)
-    assert log_probs.shape == batch_shape
-
-    # Test with batched value
-    if batch_shape:
-        value_batched = jnp.linspace(-0.5, 1.5, np.prod(batch_shape)).reshape(
-            batch_shape
-        )
-        log_probs_batched = d.log_prob(value_batched)
-        assert log_probs_batched.shape == batch_shape
-
-        # Check that values outside support return -inf
-        # Values < low should be -inf
-        below_low = low - 0.1
-        assert_allclose(d.log_prob(below_low), -jnp.inf)
-
-        # Values >= high should be -inf
-        at_high = high
-        assert_allclose(d.log_prob(at_high), -jnp.inf)
-
-
-@pytest.mark.parametrize("value_shape", [(), (5,), (3, 4), (2, 3, 4)])
-def test_uniform_log_prob_value_broadcasting(value_shape):
-    """Test broadcasting when value has different shapes."""
-    d = dist.Uniform(0.0, 1.0)
-
-    if value_shape == ():
-        values = 0.5
-    else:
-        values = jnp.linspace(-0.5, 1.5, np.prod(value_shape)).reshape(value_shape)
-
-    log_probs = d.log_prob(values)
-    assert log_probs.shape == value_shape
-
-    # Check that values inside support have finite log prob
-    inside_values = jnp.linspace(0.1, 0.9, np.prod(value_shape) if value_shape else 1)
-    if value_shape:
-        inside_values = inside_values.reshape(value_shape)
-    log_probs_inside = d.log_prob(inside_values)
-    assert jnp.all(jnp.isfinite(log_probs_inside))
-
-    # Check that values outside support have -inf
-    outside_values = jnp.linspace(-1.0, 2.0, np.prod(value_shape) if value_shape else 1)
-    if value_shape:
-        outside_values = outside_values.reshape(value_shape)
-    log_probs_outside = d.log_prob(outside_values)
-    # Values in [0, 1) should be finite, others should be -inf
-    mask_inside = (outside_values >= 0.0) & (outside_values < 1.0)
-    assert jnp.all(jnp.where(mask_inside, jnp.isfinite(log_probs_outside), True))
-    assert jnp.all(jnp.where(~mask_inside, log_probs_outside == -jnp.inf, True))
-
-
 @pytest.mark.parametrize(
     argnames="concentration1,concentration0,value",
     argvalues=[