Python stdev示例

示例#1

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))

示例#2

文件： statistics.py 项目： Brainiarc7/arrayfire-python

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))

示例#3

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)

示例#4

文件： _array.py 项目： eeeedgar/model_1

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

示例#5

文件： statistics.py 项目： arrayfire/arrayfire-python

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)

示例#6

文件： _array.py 项目： eeeedgar/model_1

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

示例#7

文件： simple_statistics.py 项目： maxssage/arrayfire-python

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))

示例#8

文件： simple_statistics.py 项目： nagyistoce/arrayfire_python

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))