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Add 'minlength' keyword to np.bincount (with docs+tests) #32

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Add 'minlength' keyword to np.bincount (with docs+tests) #32

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11 changes: 8 additions & 3 deletions numpy/add_newdocs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3796,12 +3796,15 @@

add_newdoc('numpy.lib._compiled_base', 'bincount',
"""
bincount(x, weights=None)
bincount(x, weights=None, minlength=None)

Count number of occurrences of each value in array of non-negative ints.

The number of bins (of size 1) is one larger than the largest value in
`x`. Each bin gives the number of occurrences of its index value in `x`.
`x`. If `minlength` is specified, there will be at least this number
of bins in the output array (though it will be longer if necessary,
depending on the contents of `x`).
Each bin gives the number of occurrences of its index value in `x`.
If `weights` is specified the input array is weighted by it, i.e. if a
value ``n`` is found at position ``i``, ``out[n] += weight[i]`` instead
of ``out[n] += 1``.
Expand All @@ -3812,6 +3815,8 @@
Input array.
weights : array_like, optional
Weights, array of the same shape as `x`.
minlength : integer, optional
A minimum number of bins for the output array.

Returns
-------
Expand All @@ -3823,7 +3828,7 @@
------
ValueError
If the input is not 1-dimensional, or contains elements with negative
values.
values, or if `minlength` is non-positive.
TypeError
If the type of the input is float or complex.

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32 changes: 24 additions & 8 deletions numpy/lib/src/_compiled_base.c
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Expand Up @@ -90,27 +90,29 @@ mnx (intp *i , intp len)
/*
* arr_bincount is registered as bincount.
*
* bincount accepts one or two arguments. The first is an array of
* non-negative integers and the second, if present, is an array of weights,
* which must be promotable to double. Call these arguments list and
* bincount accepts one, two or three arguments. The first is an array of
* non-negative integers The second, if present, is an array of weights,
* which must be promotable to double. Call these arguments list and
* weight. Both must be one-dimensional with len(weight) == len(list). If
* weight is not present then bincount(list)[i] is the number of occurrences
* of i in list. If weight is present then bincount(self,list, weight)[i]
* is the sum of all weight[j] where list [j] == i. Self is not used.
* The third argument, if present, is a minimum length desired for the
* output array.
*/
static PyObject *
arr_bincount(PyObject *NPY_UNUSED(self), PyObject *args, PyObject *kwds)
{
PyArray_Descr *type;
PyObject *list = NULL, *weight=Py_None;
PyObject *list = NULL, *weight=Py_None, *mlength=Py_None;
PyObject *lst=NULL, *ans=NULL, *wts=NULL;
intp *numbers, *ians, len , mxi, mni, ans_size;
intp *numbers, *ians, len , mxi, mni, ans_size, minlength;
int i;
double *weights , *dans;
static char *kwlist[] = {"list", "weights", NULL};
static char *kwlist[] = {"list", "weights", "minlength", NULL};

if (!PyArg_ParseTupleAndKeywords(args, kwds, "O|O",
kwlist, &list, &weight)) {
if (!PyArg_ParseTupleAndKeywords(args, kwds, "O|OO",
kwlist, &list, &weight, &mlength)) {
goto fail;
}
if (!(lst = PyArray_ContiguousFromAny(list, PyArray_INTP, 1, 1))) {
Expand All @@ -131,6 +133,20 @@ arr_bincount(PyObject *NPY_UNUSED(self), PyObject *args, PyObject *kwds)
goto fail;
}
ans_size = numbers [mxi] + 1;
if (mlength != Py_None) {
if (!(minlength = PyArray_PyIntAsIntp(mlength))) {
goto fail;
}
if (minlength <= 0) {
/* superfluous, but may catch incorrect usage */
PyErr_SetString(PyExc_ValueError,
"minlength, if specified, must be positive.");
goto fail;
}
if (ans_size < minlength) {
ans_size = minlength;
}
}
type = PyArray_DescrFromType(PyArray_INTP);
if (weight == Py_None) {
if (!(ans = PyArray_Zeros(1, &ans_size, type, 0))) {
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15 changes: 15 additions & 0 deletions numpy/lib/tests/test_function_base.py
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Expand Up @@ -1026,6 +1026,21 @@ def test_simple_weight2(self):
y = np.bincount(x, w)
assert_array_equal(y, np.array([0, 0.2, 0.5, 0, 0.5, 0.1]))

def test_with_minlength(self):
x = np.array([0, 1, 0, 1, 1])
y = np.bincount(x, minlength=3)
assert_array_equal(y, np.array([2, 3, 0]))

def test_with_minlength_smaller_than_maxvalue(self):
x = np.array([0, 1, 1, 2, 2, 3, 3])
y = np.bincount(x, minlength=2)
assert_array_equal(y, np.array([1, 2, 2, 2]))

def test_with_minlength_and_weights(self):
x = np.array([1, 2, 4, 5, 2])
w = np.array([0.2, 0.3, 0.5, 0.1, 0.2])
y = np.bincount(x, w, 8)
assert_array_equal(y, np.array([0, 0.2, 0.5, 0, 0.5, 0.1, 0, 0]))

class TestInterp(TestCase):
def test_exceptions(self):
Expand Down