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 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 quantile(self, *q):
"""Quantile Function
Also known as the inverse cumulative Distribution function, this function
takes known quantiles and returns the associated `X` value from the
support domain.
.. math::
\begin{cases}
0 &\text{if } 0 \leq q \lt p
1 &\text{if } p \leq q \lt 1
\end{cases}
Parameters
----------
q : numpy.ndarray, float
The probabilities within domain [0, 1]
Returns
-------
numpy.ndarray
The `X` values from the support domain associated with the input
quantiles.
"""
# check array for numpy structure
q = check_array(q, reduce_args=True, ensure_1d=True)
out = np.ceil(sc.bdtrik(q, 1, self.bias))
return np.where(self.bias >= q, 0, 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 _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 bdtrik_comp(y, n, p):
return bdtrik(1-y, n, p)
def bdtrik_comp(y, n, p):
return bdtrik(1-y, n, p)
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 _ppf(self, q_data, size, prob):
return numpy.ceil(special.bdtrik(q_data, numpy.floor(size), prob))