def simple_statistics(verbose=False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
a = af.randu(5, 5)
b = af.randu(5, 5)
w = af.randu(5, 1)
display_func(af.mean(a, dim=0))
display_func(af.mean(a, weights=w, dim=0))
print_func(af.mean(a))
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, weights=w, dim=0))
print_func(af.var(a))
print_func(af.var(a, isbiased=True))
print_func(af.var(a, weights=w))
display_func(af.stdev(a, dim=0))
print_func(af.stdev(a))
display_func(af.var(a, dim=0))
display_func(af.var(a, isbiased=True, dim=0))
print_func(af.var(a))
print_func(af.var(a, isbiased=True))
display_func(af.median(a, dim=0))
print_func(af.median(w))
print_func(af.corrcoef(a, b))
def simple_statistics(verbose=False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
a = af.randu(5, 5)
b = af.randu(5, 5)
w = af.randu(5, 1)
display_func(af.mean(a, dim=0))
display_func(af.mean(a, weights=w, dim=0))
print_func(af.mean(a))
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, weights=w, dim=0))
print_func(af.var(a))
print_func(af.var(a, isbiased=True))
print_func(af.var(a, weights=w))
display_func(af.stdev(a, dim=0))
print_func(af.stdev(a))
display_func(af.var(a, dim=0))
display_func(af.var(a, isbiased=True, dim=0))
print_func(af.var(a))
print_func(af.var(a, isbiased=True))
display_func(af.median(a, dim=0))
print_func(af.median(w))
print_func(af.corrcoef(a, b))
def simple_statistics(verbose=False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
a = af.randu(5, 5)
b = af.randu(5, 5)
w = af.randu(5, 1)
display_func(af.mean(a, dim=0))
display_func(af.mean(a, weights=w, dim=0))
print_func(af.mean(a))
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, weights=w, dim=0))
print_func(af.var(a))
print_func(af.var(a, isbiased=True))
print_func(af.var(a, weights=w))
mean, var = af.meanvar(a, dim=0)
display_func(mean)
display_func(var)
mean, var = af.meanvar(a, weights=w, bias=af.VARIANCE.SAMPLE, dim=0)
display_func(mean)
display_func(var)
display_func(af.stdev(a, dim=0))
print_func(af.stdev(a))
display_func(af.var(a, dim=0))
display_func(af.var(a, isbiased=True, dim=0))
print_func(af.var(a))
print_func(af.var(a, isbiased=True))
display_func(af.median(a, dim=0))
print_func(af.median(w))
print_func(af.corrcoef(a, b))
data = af.iota(5, 3)
k = 3
dim = 0
order = af.TOPK.DEFAULT # defaults to af.TOPK.MAX
assert (dim == 0) # topk currently supports first dim only
values, indices = af.topk(data, k, dim, order)
display_func(values)
display_func(indices)
def corrcoef(x,
y: tp.Optional = None,
rowvar: int = 1,
bias: bool = False,
ddof: tp.Optional[int] = None):
"""
Return Pearson product-moment correlation coefficients.
"""
isbiased = bias
if ddof and ddof == 0:
isbiased = True
cov_array = af.cov(x._af_array, isbiased=isbiased, dim=0)
std_array = af.stdev(x._af_array, dim=0)
if isinstance(std_array, af.Array) and isinstance(cov_array, af.Array):
new_array = cov_array / af.matmulNT(std_array, std_array)
return ndarray(new_array)
elif (isinstance(std_array, af.Array) and
not isinstance(cov_array, af.Array)) or \
(not isinstance(std_array, af.Array) and
isinstance(cov_array, af.Array)):
raise ValueError("error: either both std_array and cov_array must be "
"arrayfire arrays or none of them")
else:
return cov_array / std_array * std_array
def simple_statistics(verbose=False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
a = af.randu(5, 5)
b = af.randu(5, 5)
w = af.randu(5, 1)
display_func(af.mean(a, dim=0))
display_func(af.mean(a, weights=w, dim=0))
print_func(af.mean(a))
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, weights=w, dim=0))
print_func(af.var(a))
print_func(af.var(a, isbiased=True))
print_func(af.var(a, weights=w))
display_func(af.stdev(a, dim=0))
print_func(af.stdev(a))
display_func(af.var(a, dim=0))
display_func(af.var(a, isbiased=True, dim=0))
print_func(af.var(a))
print_func(af.var(a, isbiased=True))
display_func(af.median(a, dim=0))
print_func(af.median(w))
print_func(af.corrcoef(a, b))
data = af.iota(5, 3)
k = 3
dim = 0
order = af.TOPK.DEFAULT # defaults to af.TOPK.MAX
assert(dim == 0) # topk currently supports first dim only
values,indices = af.topk(data, k, dim, order)
display_func(values)
display_func(indices)
def std(a: ndarray, axis: tp.Optional[int] = None, ddof: int = 0) \
-> tp.Union[ndarray, numbers.Number]:
"""
Compute the standard deviation along the specified axis.
"""
new_af_array: tp.Optional[tp.Union[ndarray, numbers.Number]] = None
if ddof or ddof == 0:
new_af_array = af.stdev(a._af_array, dim=axis)
elif ddof == 1:
new_af_array = af.sqrt(af.var(a._af_array, isbiased=False, dim=axis))
if isinstance(new_af_array, af.Array):
return ndarray(new_af_array)
else:
return new_af_array
import arrayfire as af a = af.randu(5, 5) b = af.randu(5, 5) w = af.randu(5, 1) af.display(af.mean(a, dim=0)) af.display(af.mean(a, weights=w, dim=0)) print(af.mean(a)) print(af.mean(a, weights=w)) af.display(af.var(a, dim=0)) af.display(af.var(a, isbiased=True, dim=0)) af.display(af.var(a, weights=w, dim=0)) print(af.var(a)) print(af.var(a, isbiased=True)) print(af.var(a, weights=w)) af.display(af.stdev(a, dim=0)) print(af.stdev(a)) af.display(af.var(a, dim=0)) af.display(af.var(a, isbiased=True, dim=0)) print(af.var(a)) print(af.var(a, isbiased=True)) af.display(af.median(a, dim=0)) print(af.median(w)) print(af.corrcoef(a, b))
import arrayfire as af a = af.randu(5, 5) b = af.randu(5, 5) w = af.randu(5, 1) af.print_array(af.mean(a, dim=0)) af.print_array(af.mean(a, weights=w, dim=0)) print(af.mean(a)) print(af.mean(a, weights=w)) af.print_array(af.var(a, dim=0)) af.print_array(af.var(a, isbiased=True, dim=0)) af.print_array(af.var(a, weights=w, dim=0)) print(af.var(a)) print(af.var(a, isbiased=True)) print(af.var(a, weights=w)) af.print_array(af.stdev(a, dim=0)) print(af.stdev(a)) af.print_array(af.var(a, dim=0)) af.print_array(af.var(a, isbiased=True, dim=0)) print(af.var(a)) print(af.var(a, isbiased=True)) af.print_array(af.median(a, dim=0)) print(af.median(w)) print(af.corrcoef(a, b))
