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#cython: embedsignature=True
#cython: language_level=3
import numpy
cimport numpy as np
import cython
cdef extern from "numpy/npy_math.h":
bint npy_isnan(double x)
@cython.wraparound(False)
def contingency_floatarray(np.ndarray[np.float64_t, ndim=1] col_data, np.ndarray[np.float64_t, ndim=1] classes, np.intp_t n_rows, np.ndarray[np.float64_t, ndim=1] W = None):
"""
Given column values and class values, return
- an array with the sorted list of values,
- a 2D array with counts for the value (indexed by columns)
and class value (indexed by rows),
- array with the number of missing values for each class.
- array with the number of missing class values for each column value.
- and the number of missing in class and column values at same time.
"""
cdef np.ndarray[np.intp_t, ndim=1] ranks = col_data.argsort()
cdef int N = 0
cdef np.float64_t v
cdef np.float64_t last = float("NaN")
cdef Py_ssize_t i,j
cdef int weights = not W is None
for i in range(ranks.shape[0]):
i = ranks[i]
v = col_data[i]
if v != last and not npy_isnan(v):
N += 1
last = v
cdef np.ndarray[np.float64_t, ndim=1] V = numpy.zeros(N, dtype=numpy.float64)
cdef np.ndarray[np.float64_t, ndim=2] C = numpy.zeros((n_rows, N), dtype=numpy.float64)
last = float("NaN")
j = -1
cdef np.float64_t tc
cdef np.ndarray[np.float64_t, ndim=1] col_unknowns = numpy.zeros(n_rows, dtype=numpy.float64)
cdef np.ndarray[np.float64_t, ndim=1] row_unknowns = numpy.zeros(N, dtype=numpy.float64)
cdef np.float64_t unknowns = 0
for i in range(ranks.shape[0]):
i = ranks[i]
v = col_data[i]
tc = classes[i]
if v != last and not npy_isnan(v):
j += 1
V[j] = v
last = v
if npy_isnan(v) and npy_isnan(tc):
unknowns += W[i] if weights else 1.
elif npy_isnan(tc):
row_unknowns[j] += W[i] if weights else 1.
elif npy_isnan(v):
col_unknowns[int(tc)] += W[i] if weights else 1.
else:
C[int(tc),j] += W[i] if weights else 1.
assert j == N-1
return (V,C),col_unknowns,row_unknowns,unknowns
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