def ppf(self, x):
"""
Computes the percent point function of the distribution at the point(s)
x. It is defined as the inverse of the CDF. y = ppf(x) can be
interpreted as the argument y for which the value of the cdf(x) is equal
to y. Essentially that means the random varable y is the place on the
distribution the CDF evaluates to x.
Parameters
----------
x: array, dtype=float, shape=(m x n), bounds=(0,1)
The value(s) at which the user would like the ppf evaluated.
If an array is passed in, the ppf is evaluated at every point
in the array and an array of the same size is returned.
Returns
-------
ppf: array, dtype=float, shape=(m x n)
The ppf at each point in x.
"""
vals = np.ceil(bdtrik(x, self.n, self.p))
vals1 = vals - 1
temp = bdtr(vals1, self.n, self.p)
ppf = np.where((temp >= x), vals1, vals)
return ppf
def cdf(self, *X):
# check array for numpy structure
X = check_array(X, reduce_args=True, ensure_1d=True)
# floor X values
X = np.floor(X)
return sc.bdtr(X, self.n_trials, self.bias)
def _cdf_single(self, x, n, a, b):
k = floor(x)
p = linspace(0, 1, num=10001)
bta = btdtr(a, b, p)
p_med = (p[:-1] + p[1:]) / 2
bta_med = bta[1:] - bta[:-1]
vals = (bdtr(k, n, p_med) * bta_med).sum(axis=-1)
return vals
def _cdf(self, x_data, size, prob):
size = numpy.round(size)
x_data = x_data - 0.5
floor = numpy.zeros(x_data.shape)
indices = x_data >= 0
floor[indices] = special.bdtr(numpy.floor(x_data[indices]), size, prob)
ceil = numpy.ones(x_data.shape)
indices = x_data <= size
ceil[indices] = special.bdtr(numpy.ceil(x_data[indices]), size, prob)
ceil[numpy.isnan(ceil)] = 0 # left edge case
offset = x_data - numpy.floor(x_data)
return floor * (1 - offset) + ceil * offset
offset = x_data - x_data_int + 0.5
assert numpy.all(offset >= 0) and numpy.all(offset <= 1), (
"somethings up with the offset: %s" % offset)
x_data_int = numpy.clip(x_data_int, 0, size)
out = out_lower * (1 - offset) + out_upper * offset
return out
def quantile(self, *q):
# check array for numpy structure
q = check_array(q, reduce_args=True, ensure_1d=True)
# get the upper value of X (ceiling)
X_up = np.ceil(sc.bdtrik(q, self.n_trials, self.bias))
# get the lower value of X (floor)
X_down = np.maximum(X_up - 1, 0)
# recompute quantiles to validate transformation
q_test = sc.bdtr(X_down, self.n_trials, self.bias)
# when q_test is greater than true, shift output down
out = np.where(q_test >= q, X_down, X_up).astype(int)
# return only in-bound values
return np.where(self.support.contains(out), out, np.nan)
def cdf(self, x):
"""
Computes the cumulative distribution function of the
distribution at the point(s) x. The cdf is defined as follows:
F(x|p) = 1 - (1 - p) ** x
Parameters
----------
x: array, dtype=float, shape=(m x n)
The value(s) at which the user would like the cdf evaluated.
If an array is passed in, the cdf is evaluated at every point
in the array and an array of the same size is returned.
Returns
-------
cdf: array, dtype=float, shape=(m x n)
The cdf at each point in x.
"""
k = np.floor(x)
cdf = bdtr(k, self.n, self.p)
return cdf
def cdf(self, x):
"""
Computes the cumulative distribution function of the
distribution at the point(s) x. The cdf is defined as follows:
F(x|p) = 1 - (1 - p) ** x
Parameters
----------
x: array, dtype=float, shape=(m x n)
The value(s) at which the user would like the cdf evaluated.
If an array is passed in, the cdf is evaluated at every point
in the array and an array of the same size is returned.
Returns
-------
cdf: array, dtype=float, shape=(m x n)
The cdf at each point in x.
"""
k = np.floor(x)
cdf = bdtr(k, self.n, self.p)
return cdf
def test_bdtr_bdtri_roundtrip(self):
bdtr_vals = sc.bdtr([0, 1, 2], 2, 0.5)
roundtrip_vals = sc.bdtri([0, 1, 2], 2, bdtr_vals)
assert_allclose(roundtrip_vals, [0.5, 0.5, np.nan])
def test(self):
val = sc.bdtr(0, 1, 0.5)
assert_allclose(val, 0.5)
def test_bdtr_bdtrc_sum_to_one(self):
bdtr_vals = sc.bdtr([0, 1, 2], 2, 0.5)
bdtrc_vals = sc.bdtrc([0, 1, 2], 2, 0.5)
vals = bdtr_vals + bdtrc_vals
assert_allclose(vals, [1.0, 1.0, 1.0])
def test_domain(self):
val = sc.bdtr(-1.1, 1, 0.5)
assert np.isnan(val)
def test_inf(self, k, n, p):
with suppress_warnings() as sup:
sup.filter(DeprecationWarning)
val = sc.bdtr(k, n, p)
assert np.isnan(val)
def test_rounding(self):
double_val = sc.bdtr([0.1, 1.1, 2.1], 2, 0.5)
int_val = sc.bdtr([0, 1, 2], 2, 0.5)
assert_array_equal(double_val, int_val)
def ref(self, x, y, z):
return special.bdtr(x, y, z)
def _cdf(self, x, n, p):
k = floor(x)
vals = special.bdtr(k, n, p)
return vals
def _cdf(self, x, n, p):
k = floor(x)
vals = special.bdtr(k, n, p)
return vals
def addsubtract(a, b):
if a > b:
return a - b
else:
return a + b
vec_addsubtract = np.vectorize(addsubtract)
r = vec_addsubtract([0, 3, 6, 9], [1, 3, 5, 7])
print(r)
np.cast['f'](np.pi)
x = np.r_[-2:3]
y = np.select([x > 3, x >= 0], [0, x + 2])
print(y)
print(bdtr(-1, 10, 0.3))
# 1-D interpolation (interp1d)
x = np.linspace(0, 10, num=11, endpoint=True)
y = np.cos(-x**2 / 9.0)
f = interp1d(x, y)
f2 = interp1d(x, y, kind='cubic')
xnew = np.linspace(0, 10, num=41, endpoint=True)
plt.plot(x, y, 'o', xnew, f(xnew), '-', xnew, f2(xnew), '--')
plt.legend(['data', 'linear', 'cubic'], loc='best')
plt.show()
def _ppf(self, q, n, p):
vals = ceil(special.bdtrik(q, n, p))
vals1 = np.maximum(vals - 1, 0)
temp = special.bdtr(vals1, n, p)
return np.where(temp >= q, vals1, vals)
def _cdf(self, x_data, size, prob):
return special.bdtr(numpy.floor(x_data), numpy.floor(size), prob)
def _ppf(self, q, n, p):
vals = ceil(special.bdtrik(q, n, p))
vals1 = np.maximum(vals - 1, 0)
temp = special.bdtr(vals1, n, p)
return np.where(temp >= q, vals1, vals)
def test_sum_is_one(self):
val = sc.bdtr([0, 1, 2], 2, 0.5)
assert_array_equal(val, [0.25, 0.75, 1.0])
