Python numpy.expm1方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import expm1 [as 別名]
def test_burkardt_3(self):
        # This example is due to Laub.
        # This matrix is ill-suited for the Taylor series approach.
        # As powers of A are computed, the entries blow up too quickly.
        exp1 = np.exp(1)
        exp39 = np.exp(39)
        A = np.array([
            [0, 1],
            [-39, -40],
            ], dtype=float)
        desired = np.array([
            [
                39/(38*exp1) - 1/(38*exp39),
                -np.expm1(-38) / (38*exp1)],
            [
                39*np.expm1(-38) / (38*exp1),
                -1/(38*exp1) + 39/(38*exp39)],
            ], dtype=float)
        actual = expm(A)
        assert_allclose(actual, desired) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import expm1 [as 別名]
def ipsi(self, u, log=False):
        r = np.asarray(u) * self.params

        res = np.copy(r)
        res[np.isnan(r)] = np.nan
        em = np.expm1(-self.params)

        #  for small inputs, u <= 0.01
        small_mask = np.abs(r) <= 0.01 * abs(self.params)
        res[small_mask] = -np.log(np.expm1(-r[small_mask]) / em)

        big_mask = np.abs(r) > 0.01 * abs(self.params)
        e = np.exp(-self.params)
        mid_mask = (e > 0) & (np.abs(self.params - r) < 0.5)  # theta * (1 - u) < 0.5

        m1 = big_mask & mid_mask
        m2 = big_mask & ~mid_mask
        r[m1] = -np.log1p(e * np.expm1((self.params - r[m1])) / em)
        r[m2] = -np.log1p((np.exp(-r[m2]) - e) / em)

        return np.log(r) if log else r 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import expm1 [as 別名]
def random(self, n: int, seed: int = None):
        u = random_uniform(n, self.dim, seed)
        if abs(self.params) < 1e-7:
            return u

        if self.dim == 2:
            v = u[:, 1]
            a = -abs(self.params)
            v = -1 / a * np.log1p(-v * np.expm1(-a) / (np.exp(-a * u[:, 0]) * (v - 1) - v))
            u[:, 1] = 1 - v if self.params > 0 else v
            return u

        # alpha too large
        if log1mexp(self.params) == 0:
            return np.ones((n, self.dim))

        fr = random_log_series_ln1p(-self.params, n)[:, None]
        return self.psi(-np.log(u) / fr) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import expm1 [as 別名]
def _gpinv(probs, kappa, sigma):
    """Inverse Generalized Pareto distribution function."""
    # pylint: disable=unsupported-assignment-operation, invalid-unary-operand-type
    x = np.full_like(probs, np.nan)
    if sigma <= 0:
        return x
    ok = (probs > 0) & (probs < 1)
    if np.all(ok):
        if np.abs(kappa) < np.finfo(float).eps:
            x = -np.log1p(-probs)
        else:
            x = np.expm1(-kappa * np.log1p(-probs)) / kappa
        x *= sigma
    else:
        if np.abs(kappa) < np.finfo(float).eps:
            x[ok] = -np.log1p(-probs[ok])
        else:
            x[ok] = np.expm1(-kappa * np.log1p(-probs[ok])) / kappa
        x *= sigma
        x[probs == 0] = 0
        if kappa >= 0:
            x[probs == 1] = np.inf
        else:
            x[probs == 1] = -sigma / kappa
    return x 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import expm1 [as 別名]
def _test(vectorizer, model, n_rows):
    tr = load_train('tests/train_10k.tsv')
    tr, va = train_test_split(tr)
    te = pd.read_csv('tests/test_10k_corrupted.tsv', sep="\t")
    if n_rows is not None:
        if n_rows == 'random':
            n_rows = np.random.randint(1, te.shape[0])
            te = te.sample(n=n_rows)
    mat_tr = vectorizer.fit_transform(tr, tr.price)
    mat_te = vectorizer.transform(te.copy())
    mat_va = vectorizer.transform(va)
    model.fit(mat_tr, np.log1p(tr.price))
    assert rmsle(np.expm1(model.predict(mat_va)), va.price) < 0.85
    te_preds = np.expm1(model.predict(mat_te))
    assert te_preds.shape[0] == te.shape[0]
    assert np.all(np.isfinite(te_preds))
    assert te_preds.min() >= -1, "min price is {}".format(te_preds.min())
    assert te_preds.max() <= 3000, "max price is {}".format(te_preds.max()) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import expm1 [as 別名]
def bdtrc(k, n, p):
    if (k < 0):
        return (1.0)

    if (k == n):
        return (0.0)
    dn = n - k
    if (k == 0):
        if (p < .01):
            dk = -np.expm1(dn * np.log1p(-p))
        else:
            dk = 1.0 - np.exp(dn * np.log(1.0 - p))
    else:
        dk = k + 1
        dk = betainc(dk, dn, p)
    return dk 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import expm1 [as 別名]
def finv(self, f):
        return np.where(f>_lim_val, f, np.log(np.expm1(f))) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import expm1 [as 別名]
def gradfactor(self, f, df):
        return df*np.where(f>_lim_val, 1.,  - np.expm1(-f)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import expm1 [as 別名]
def log_jacobian(self, model_param):
        return np.where(model_param>_lim_val, model_param, np.log(np.expm1(model_param))) - model_param 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import expm1 [as 別名]
def log_jacobian_grad(self, model_param):
        return 1./(np.expm1(model_param)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import expm1 [as 別名]
def _cdf(self, x, p):
        k = floor(x)
        return -expm1(log1p(-p)*k) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import expm1 [as 別名]
def _stats(self, lambda_):
        mu = 1/(exp(lambda_)-1)
        var = exp(-lambda_)/(expm1(-lambda_))**2
        g1 = 2*cosh(lambda_/2.0)
        g2 = 4+2*cosh(lambda_)
        return mu, var, g1, g2 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import expm1 [as 別名]
def test_numpy_method():
    # This type of code is used frequently by PyMC3 users
    x = tt.dmatrix('x')
    data = np.random.rand(5, 5)
    x.tag.test_value = data
    for fct in [np.arccos, np.arccosh, np.arcsin, np.arcsinh,
                np.arctan, np.arctanh, np.ceil, np.cos, np.cosh, np.deg2rad,
                np.exp, np.exp2, np.expm1, np.floor, np.log,
                np.log10, np.log1p, np.log2, np.rad2deg,
                np.sin, np.sinh, np.sqrt, np.tan, np.tanh, np.trunc]:
        y = fct(x)
        f = theano.function([x], y)
        utt.assert_allclose(np.nan_to_num(f(data)),
                            np.nan_to_num(fct(data))) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import expm1 [as 別名]
def impl(self, x):
        # If x is an int8 or uint8, numpy.expm1 will compute the result in
        # half-precision (float16), where we want float32.
        x_dtype = str(getattr(x, 'dtype', ''))
        if x_dtype in ('int8', 'uint8'):
            return numpy.expm1(x, sig='f')
        return numpy.expm1(x) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import expm1 [as 別名]
def c_code(self, node, name, inputs, outputs, sub):
        (x,) = inputs
        (z,) = outputs
        if node.inputs[0].type in complex_types:
            raise NotImplementedError('type not supported', type)
        return "%(z)s = expm1(%(x)s);" % locals()