diff --git a/arrayfire/algorithm.py b/arrayfire/algorithm.py
index d5adbcce5..6f06cee14 100644
--- a/arrayfire/algorithm.py
+++ b/arrayfire/algorithm.py
@@ -250,6 +250,34 @@ def max(a, dim=None):
else:
return _reduce_all(a, backend.get().af_max_all)
+def maxRagged(vals, lens, dim):
+ """
+ Find the maximum value of a subset of elements along a specified dimension.
+
+ The size of the subset of elements along the given dimension are decided based on the lengths
+ provided in the `lens` array.
+
+ Parameters
+ ----------
+ vals : af.Array
+ Multi dimensional arrayfire array.
+ lens : af.Array
+ Multi dimensional arrayfire array containing number of elements to reduce along given `dim`
+ dim: optional: int. default: None
+ Dimension along which the maximum value is required.
+
+ Returns
+ -------
+ (values, indices): A tuple of af.Array(s)
+ `values` af.Array will have the maximum values along given dimension for
+ subsets determined by lengths provided in `lens`
+ `idx` contains the locations of the maximum values as per the lengths provided in `lens`
+ """
+ out_vals = Array()
+ out_idx = Array()
+ safe_call(backend().get().af_max_ragged(c_pointer(out_vals.arr), c_pointer(out_idx.arr), c_pointer(vals.arr), c_pointer(lens.arr), c_int_t(dim)))
+ return out_vals, out_idx
+
def maxByKey(keys, vals, dim=-1):
"""
Calculate the max of elements along a specified dimension according to a key.
diff --git a/arrayfire/cuda.py b/arrayfire/cuda.py
index ea24c0e30..e5ef819be 100644
--- a/arrayfire/cuda.py
+++ b/arrayfire/cuda.py
@@ -85,3 +85,10 @@ def set_native_id(idx):
safe_call(backend.get().afcu_set_native_id(idx))
return
+
+def set_cublas_mode(mode=CUBLAS_MATH_MODE.DEFAULT):
+ """
+ Set's cuBLAS math mode for CUDA backend. In other backends, this has no effect.
+ """
+ safe_call(backend().get().afcu_cublasSetMathMode(mode.value))
+ return
diff --git a/arrayfire/library.py b/arrayfire/library.py
index 27ca4f4d9..1b3c8b3ea 100644
--- a/arrayfire/library.py
+++ b/arrayfire/library.py
@@ -490,6 +490,13 @@ class VARIANCE(_Enum):
SAMPLE = _Enum_Type(1)
POPULATION = _Enum_Type(2)
+class CUBLAS_MATH_MODE(_Enum):
+ """
+ Enable Tensor Core usage if available on CUDA backend GPUs
+ """
+ DEFAULT = _Enum_Type(0)
+ TENSOR_OP = _Enum_Type(1)
+
_VER_MAJOR_PLACEHOLDER = "__VER_MAJOR__"
def _setup():
diff --git a/arrayfire/statistics.py b/arrayfire/statistics.py
index f47f3a48d..158da18de 100644
--- a/arrayfire/statistics.py
+++ b/arrayfire/statistics.py
@@ -59,7 +59,7 @@ def mean(a, weights=None, dim=None):
return real if imag == 0 else real + imag * 1j
-def var(a, isbiased=False, weights=None, dim=None):
+def var(a, bias=VARIANCE.DEFAULT, weights=None, dim=None):
"""
Calculate variance along a given dimension.
@@ -68,9 +68,9 @@ def var(a, isbiased=False, weights=None, dim=None):
a: af.Array
The input array.
- isbiased: optional: Boolean. default: False.
- Boolean denoting population variance (false) or sample
- variance (true).
+ bias: optional: af.VARIANCE. default: DEFAULT.
+ population variance(VARIANCE.POPULATION) or sample variance(VARIANCE.SAMPLE).
+ This is ignored if weights are provided.
weights: optional: af.Array. default: None.
Array to calculate for the weighted mean. Must match size of
@@ -89,7 +89,7 @@ def var(a, isbiased=False, weights=None, dim=None):
out = Array()
if weights is None:
- safe_call(backend.get().af_var(c_pointer(out.arr), a.arr, isbiased, c_int_t(dim)))
+ safe_call(backend.get().af_var_v2(c_pointer(out.arr), a.arr, bias.value, c_int_t(dim)))
else:
safe_call(backend.get().af_var_weighted(c_pointer(out.arr), a.arr, weights.arr, c_int_t(dim)))
@@ -99,7 +99,7 @@ def var(a, isbiased=False, weights=None, dim=None):
imag = c_double_t(0)
if weights is None:
- safe_call(backend.get().af_var_all(c_pointer(real), c_pointer(imag), a.arr, isbiased))
+ safe_call(backend.get().af_var_all_v2(c_pointer(real), c_pointer(imag), a.arr, bias.value))
else:
safe_call(backend.get().af_var_all_weighted(c_pointer(real), c_pointer(imag), a.arr, weights.arr))
@@ -150,7 +150,7 @@ def meanvar(a, weights=None, bias=VARIANCE.DEFAULT, dim=-1):
return mean_out, var_out
-def stdev(a, dim=None):
+def stdev(a, bias=VARIANCE.DEFAULT, dim=None):
"""
Calculate standard deviation along a given dimension.
@@ -159,6 +159,10 @@ def stdev(a, dim=None):
a: af.Array
The input array.
+ bias: optional: af.VARIANCE. default: DEFAULT.
+ population variance(VARIANCE.POPULATION) or sample variance(VARIANCE.SAMPLE).
+ This is ignored if weights are provided.
+
dim: optional: int. default: None.
The dimension for which to obtain the standard deviation from
input data.
@@ -171,48 +175,41 @@ def stdev(a, dim=None):
"""
if dim is not None:
out = Array()
- safe_call(backend.get().af_stdev(c_pointer(out.arr), a.arr, c_int_t(dim)))
+ safe_call(backend.get().af_stdev_v2(c_pointer(out.arr), a.arr, bias.value,
+ c_int_t(dim)))
return out
else:
real = c_double_t(0)
imag = c_double_t(0)
- safe_call(backend.get().af_stdev_all(c_pointer(real), c_pointer(imag), a.arr))
+ safe_call(backend.get().af_stdev_all_v2(c_pointer(real), c_pointer(imag), a.arr,
+ bias.value))
real = real.value
imag = imag.value
return real if imag == 0 else real + imag * 1j
-def cov(a, isbiased=False, dim=None):
+def cov(a, b, bias=VARIANCE.DEFAULT):
"""
Calculate covariance along a given dimension.
Parameters
----------
a: af.Array
- The input array.
+ Input array.
- isbiased: optional: Boolean. default: False.
- Boolean denoting whether biased estimate should be taken.
+ b: af.Array
+ Input array.
- dim: optional: int. default: None.
- The dimension for which to obtain the covariance from input data.
+ bias: optional: af.VARIANCE. default: DEFAULT.
+ population variance(VARIANCE.POPULATION) or sample variance(VARIANCE.SAMPLE).
Returns
-------
output: af.Array
- Array containing the covariance of the input array along a
- given dimension.
+ Array containing the covariance of the input array along a given dimension.
"""
- if dim is not None:
- out = Array()
- safe_call(backend.get().af_cov(c_pointer(out.arr), a.arr, isbiased, c_int_t(dim)))
- return out
- else:
- real = c_double_t(0)
- imag = c_double_t(0)
- safe_call(backend.get().af_cov_all(c_pointer(real), c_pointer(imag), a.arr, isbiased))
- real = real.value
- imag = imag.value
- return real if imag == 0 else real + imag * 1j
+ out = Array()
+ safe_call(backend.get().af_cov_v2(c_pointer(out.arr), a.arr, b.arr, bias.value))
+ return out
def median(a, dim=None):
"""
diff --git a/tests/simple/statistics.py b/tests/simple/statistics.py
index 174af0a5b..39fe6703f 100644
--- a/tests/simple/statistics.py
+++ b/tests/simple/statistics.py
@@ -28,10 +28,10 @@ def simple_statistics(verbose=False):
print_func(af.mean(a, weights=w))
display_func(af.var(a, dim=0))
- display_func(af.var(a, isbiased=True, dim=0))
+ display_func(af.var(a, bias=af.VARIANCE.SAMPLE, dim=0))
display_func(af.var(a, weights=w, dim=0))
print_func(af.var(a))
- print_func(af.var(a, isbiased=True))
+ print_func(af.var(a, bias=af.VARIANCE.SAMPLE))
print_func(af.var(a, weights=w))
mean, var = af.meanvar(a, dim=0)
@@ -45,9 +45,9 @@ def simple_statistics(verbose=False):
print_func(af.stdev(a))
display_func(af.var(a, dim=0))
- display_func(af.var(a, isbiased=True, dim=0))
+ display_func(af.var(a, bias=af.VARIANCE.SAMPLE, dim=0))
print_func(af.var(a))
- print_func(af.var(a, isbiased=True))
+ print_func(af.var(a, bias=af.VARIANCE.SAMPLE))
display_func(af.median(a, dim=0))
print_func(af.median(w))
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