Python strategies.floats方法代碼示例

# 需要導入模塊: from hypothesis import strategies [as 別名]
# 或者: from hypothesis.strategies import floats [as 別名]
def ibm_compatible_floats(draw, min_value=None, max_value=None):
    if min_value is None:
        min_value = MIN_IBM_FLOAT
        
    if max_value is None:
        max_value = MAX_IBM_FLOAT
    
    truncated_min_f = max(min_value, MIN_IBM_FLOAT)
    truncated_max_f = min(max_value, MAX_IBM_FLOAT)

    strategies = []
    if truncated_min_f <= LARGEST_NEGATIVE_NORMAL_IBM_FLOAT <= truncated_max_f:
        strategies.append(floats(truncated_min_f, LARGEST_NEGATIVE_NORMAL_IBM_FLOAT))

    if truncated_min_f <= SMALLEST_POSITIVE_NORMAL_IBM_FLOAT <= truncated_max_f:
        strategies.append(floats(SMALLEST_POSITIVE_NORMAL_IBM_FLOAT, truncated_max_f))

    if truncated_min_f <= 0 <= truncated_max_f:
        strategies.append(just(0.0))

    if len(strategies) == 0:
        strategies.append(floats(truncated_min_f, truncated_max_f))

    ibm = draw(one_of(*strategies))
    return ibm 

# 需要導入模塊: from hypothesis import strategies [as 別名]
# 或者: from hypothesis.strategies import floats [as 別名]
def _strategy_2d_array(dtype, minval=0, maxval=None, **kwargs):
    if 'min_side' in kwargs:
        min_side = kwargs.pop('min_side')
    else:
        min_side = 1

    if 'max_side' in kwargs:
        max_side = kwargs.pop('max_side')
    else:
        max_side = None

    if dtype is np.int:
        elems = st.integers(minval, maxval, **kwargs)
    elif dtype is np.float:
        elems = st.floats(minval, maxval, **kwargs)
    elif dtype is np.str:
        elems = st.text(min_size=minval, max_size=maxval, **kwargs)
    else:
        raise ValueError('no elements strategy for dtype', dtype)

    return arrays(dtype, array_shapes(2, 2, min_side, max_side), elements=elems) 

# 需要導入模塊: from hypothesis import strategies [as 別名]
# 或者: from hypothesis.strategies import floats [as 別名]
def from_schema(schema):
    """Returns a strategy for objects that match the given schema."""
    check_schema(schema)
    # TODO: actually handle constraints on number/string/array schemas
    return dict(
        null=st.none(),
        bool=st.booleans(),
        number=st.floats(allow_nan=False),
        string=st.text(),
        array=st.lists(st.nothing()),
    )[schema["type"]]


# `@st.composite` is one way to write this - another would be to define a
# bare function, and `return st.one_of(st.none(), st.booleans(), ...)` so
# each strategy can be defined individually.  Use whichever seems more
# natural to you - the important thing in tests is usually readability! 

# 需要導入模塊: from hypothesis import strategies [as 別名]
# 或者: from hypothesis.strategies import floats [as 別名]
def test_spherical_list(nev):
    """The same_len_lists can draw values that when squared overflow floats.
    For regular floats, this doesn't flag, but numpy warns on it. It's
    considered a caller problem if the value does overflow.

    TODO
    ----
    Consider either properly warning or consistently failing when a partial
    computation overflows. A solution could be to coerce even regular floats to
    numpy, and look for the warning. For now, do the python thing and just
    carry on.
    """
    n, e, v = nev
    inc, azi = spherical(n, e, v)
    assert np.all(0 <= azi)
    assert np.all(azi < 360) 

# 需要導入模塊: from hypothesis import strategies [as 別名]
# 或者: from hypothesis.strategies import floats [as 別名]
def test_padding(ndim: int, data: st.DataObject):
    """Ensure that convolving a padding-only image with a commensurate kernel yields the single entry: 0"""
    padding = data.draw(
        st.integers(1, 3) | st.tuples(*[st.integers(1, 3)] * ndim), label="padding"
    )
    x = Tensor(
        data.draw(
            hnp.arrays(shape=(1, 1) + (0,) * ndim, dtype=float, elements=st.floats()),
            label="x",
        )
    )
    pad_tuple = padding if isinstance(padding, tuple) else (padding,) * ndim
    kernel = data.draw(
        hnp.arrays(
            shape=(1, 1) + tuple(2 * p for p in pad_tuple),
            dtype=float,
            elements=st.floats(allow_nan=False, allow_infinity=False),
        )
    )
    out = conv_nd(x, kernel, padding=padding, stride=1)
    assert out.shape == (1,) * x.ndim
    assert out.item() == 0.0

    out.sum().backward()
    assert x.grad.shape == x.shape 

# 需要導入模塊: from hypothesis import strategies [as 別名]
# 或者: from hypothesis.strategies import floats [as 別名]
def test_negative_log_likelihood(data: st.DataObject, labels_as_tensor: bool):
    s = data.draw(
        hnp.arrays(
            shape=hnp.array_shapes(max_side=10, min_dims=2, max_dims=2),
            dtype=float,
            elements=st.floats(-100, 100),
        )
    )
    y_true = data.draw(
        hnp.arrays(
            shape=(s.shape[0],),
            dtype=hnp.integer_dtypes(),
            elements=st.integers(min_value=0, max_value=s.shape[1] - 1),
        ).map(Tensor if labels_as_tensor else lambda x: x)
    )
    scores = Tensor(s)
    nll = negative_log_likelihood(mg.log(mg.nnet.softmax(scores)), y_true)
    nll.backward()

    cross_entropy_scores = Tensor(s)
    ce = softmax_crossentropy(cross_entropy_scores, y_true)
    ce.backward()

    assert_allclose(nll.data, ce.data, atol=1e-5, rtol=1e-5)
    assert_allclose(scores.grad, cross_entropy_scores.grad, atol=1e-5, rtol=1e-5) 

# 需要導入模塊: from hypothesis import strategies [as 別名]
# 或者: from hypothesis.strategies import floats [as 別名]
def arrays(self, i: int) -> st.SearchStrategy:
        """
        Hypothesis search strategy for drawing an array y to be passed to f(x, ..., y_i,...).
        By default, y is drawn to have a shape that is broadcast-compatible with x.

        Parameters
        ----------
        i : int
            The argument index-location of y in the signature of f.

        Returns
        -------
        hypothesis.searchstrategy.SearchStrategy"""
        return hnp.arrays(
            shape=self.index_to_arr_shapes.get(i),
            dtype=float,
            elements=st.floats(*self.index_to_bnds.get(i, self.default_bnds)),
        ) 

# 需要導入模塊: from hypothesis import strategies [as 別名]
# 或者: from hypothesis.strategies import floats [as 別名]
def generate_dictionary_with_fixed_tokens(draw):
    """
    Builds random nested dictionary structure which is then used as JSON to
    mask two fixed "token" keys.

    Structure is based on TEST_JSON sample fixture defined above.
    """
    base = draw(
        st.fixed_dictionaries({'token': st.text(printable, min_size=10)})
    )

    optional = draw(
        st.nothing() | st.dictionaries(
            st.text(ascii_letters, min_size=1),
            st.floats() | st.integers() | st.text(printable) | st.booleans()
            | st.nothing(),
            min_size=10,
            max_size=50
        )
    )

    return {**base, **optional} 

# 需要導入模塊: from hypothesis import strategies [as 別名]
# 或者: from hypothesis.strategies import floats [as 別名]
def close_enough(x, y, equal_nan=False, rtol=1e-5, atol=1e-8):
    # Might want to adjust rtol and atol for lower precision floats
    x, y = np.asarray(x), np.asarray(y)

    if x.shape != y.shape:
        return False

    if x.dtype != y.dtype:
        return False

    if x.dtype.kind == "f":
        assert y.dtype.kind == "f"
        # Note: equal_nan only considered in both float case!
        return np.allclose(x, y, equal_nan=equal_nan, rtol=rtol, atol=atol)

    return np.all(x == y) 

# 需要導入模塊: from hypothesis import strategies [as 別名]
# 或者: from hypothesis.strategies import floats [as 別名]
def choice(values):
    """
    One value from a limited set.

    Args:
        values:
            Iterable with values that will be produced by strategy. Python enums
            are iterable and thus can be used as arguments for this strategy.

    Examples:
        >>> from hypothesis import strategies as st, given
        >>> from math import sin, cos
        >>> @given(choice([sin, cos]), st.floats(-1000, 1000))
        ... def check_range(fn, x):
        ...     assert -1 <= fn(x) <= 1
    """
    values = list(values)
    return st.integers(min_value=0, max_value=len(values) - 1).map(values.__getitem__) 

# 需要導入模塊: from hypothesis import strategies [as 別名]
# 或者: from hypothesis.strategies import floats [as 別名]
def strategy(self):
        if self._multiple_of is not None:
            maximum = self._maximum
            if maximum is not None:
                maximum = math.floor(maximum / self._multiple_of)
            minimum = self._minimum
            if minimum is not None:
                minimum = math.ceil(minimum / self._multiple_of)
            strategy = hy_st.integers(min_value=minimum, max_value=maximum)
            strategy = strategy.map(lambda x: x * self._multiple_of)
        else:
            strategy = hy_st.floats(min_value=self._minimum,
                                    max_value=self._maximum)
        if self._exclusive_maximum:
            strategy = strategy.filter(lambda x: x < self._maximum)
        if self._exclusive_minimum:
            strategy = strategy.filter(lambda x: x > self._minimum)

        return strategy 

# 需要導入模塊: from hypothesis import strategies [as 別名]
# 或者: from hypothesis.strategies import floats [as 別名]
def enums_of_primitives(draw):
    """Generate enum classes with primitive values."""
    names = draw(st.sets(st.text(min_size=1), min_size=1))
    n = len(names)
    vals = draw(
        st.one_of(
            st.sets(
                st.one_of(
                    st.integers(),
                    st.floats(allow_nan=False),
                    st.text(min_size=1),
                ),
                min_size=n,
                max_size=n,
            )
        )
    )
    return Enum("HypEnum", list(zip(names, vals))) 

# 需要導入模塊: from hypothesis import strategies [as 別名]
# 或者: from hypothesis.strategies import floats [as 別名]
def ibm_compatible_negative_floats(draw):
    return draw(floats(MIN_IBM_FLOAT, LARGEST_NEGATIVE_NORMAL_IBM_FLOAT)) 

# 需要導入模塊: from hypothesis import strategies [as 別名]
# 或者: from hypothesis.strategies import floats [as 別名]
def ibm_compatible_positive_floats(draw):
    return draw(floats(SMALLEST_POSITIVE_NORMAL_IBM_FLOAT, MAX_IBM_FLOAT)) 

# 需要導入模塊: from hypothesis import strategies [as 別名]
# 或者: from hypothesis.strategies import floats [as 別名]
def ibm_compatible_non_negative_floats(draw):
    return draw(one_of(
        just(0.0),
        floats(SMALLEST_POSITIVE_NORMAL_IBM_FLOAT, MAX_IBM_FLOAT)))