[转载] numpy教程：逻辑函数Logic functions

参考链接： Python中的numpy.isfortran

http://blog.csdn.net/pipisorry/article/details/48208433

真值测试Truth value testing

all(a[, axis, out, keepdims])Test whether all array elements along a given axis evaluate to True.any(a[, axis, out, keepdims])Test whether any array element along a given axis evaluates to True.

只要数组中有一个值为True，则any()返回True；而只有数组的全部元素都为True，all()才返回True。

也可以直接当成array数组的方法使用。

判断numpy数组是否为空

if a.size: print('array is not empty')

如果通过python列表，把一个列表作为一个布尔值会产生True如果有项目，False如果它是空的。lst = []if lst: print "array has items"if not lst: print "array is empty"

[Python的-如何检查数组不为空？]

判断numpy数组中是否有True

array.any()

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数组内容Array contents

isfinite(x[, out])Test element-wise for finiteness (not infinity or not Not a Number).isinf(x[, out])Test element-wise for positive or negative infinity.isnan(x[, out])Test element-wise for NaN and return result as a boolean array.isneginf(x[, y])Test element-wise for negative infinity, return result as bool array.isposinf(x[, y])Test element-wise for positive infinity, return result as bool array.

numpy.isnan

numpy判断一个元素是否为np.NaN，判断某元素是否是nan

numpy.isnan(element)

Note: 不能使用array[0] == np.NaN，总是返回False!

numpy数组元素替换numpy.nan_to_num(x)

判断某元素是否是nan,inf,neginf,如果是，nan换为0，inf换为一个非常大的数，neginf换为非常小的数

numpy.nan_to_num(x)Replace nan with zero and inf with finite numbers.Returns an array or scalar replacing Not a Number (NaN) with zero, (positive) infinity with a very large number and negative infinity with a very small (or negative) number.

数组类型测试Array type testing

iscomplex(x)Returns a bool array, where True if input element is complex.iscomplexobj(x)Check for a complex type or an array of complex numbers.isfortran(a)Returns True if the array is Fortran contiguous but not C contiguous.isreal(x)Returns a bool array, where True if input element is real.isrealobj(x)Return True if x is a not complex type or an array of complex numbers.isscalar(num)Returns True if the type of num is a scalar type.

逻辑操作Logical operations

logical_and(x1, x2[, out])Compute the truth value of x1 AND x2 element-wise.logical_or(x1, x2[, out])Compute the truth value of x1 OR x2 element-wise.logical_not(x[, out])Compute the truth value of NOT x element-wise.logical_xor(x1, x2[, out])Compute the truth value of x1 XOR x2, element-wise.

两个0-1array相与操作

判断两个0-1array有多少个相同的1，两种方式

rate = np.count_nonzero(np.logical_and(fs_predict_array, ground_truth_array))rate = np.count_nonzero(fs_predict_array * ground_truth_array)不过fs_predict_array

* ground_truth_array返回的是0-1array，而np.logical_and(fs_predict_array ,ground_truth_array)返回的是True-False array，但是都可以使用sum()得到1或者True的数目。

lz亲测下面的logical_and操作运行速度更快，没有count_nonzero会更快。

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比较Comparison

allclose(a, b[, rtol, atol, equal_nan])Returns True if two arrays are element-wise equal within a tolerance.isclose(a, b[, rtol, atol, equal_nan])Returns a boolean array where two arrays are element-wise equal within a tolerance.array_equal(a1, a2)True if two arrays have the same shape and elements, False otherwise.array_equiv(a1, a2)Returns True if input arrays are shape consistent and all elements equal.

greater(x1, x2[, out])Return the truth value of (x1 > x2) element-wise.greater_equal(x1, x2[, out])Return the truth value of (x1 >= x2) element-wise.less(x1, x2[, out])Return the truth value of (x1 < x2) element-wise.less_equal(x1, x2[, out])Return the truth value of (x1 =< x2) element-wise.equal(x1, x2[, out])Return (x1 == x2) element-wise.not_equal(x1, x2[, out])Return (x1 != x2) element-wise.

allclose

如果两个数组在tolerance误差范围内相等，则返回True。

from: http://blog.csdn.net/pipisorry/article/details/48208433

ref: Logic functions

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