numpy logical_and reduce
,numpy. logical_and (x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'logical_and'>¶. ,numpy. logical_and (x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'logical_and'>¶. ,Parameters: x1, x2 : array_like. Input arrays. x1 and x2 must be of the same shape. out : ndarray, None, or tuple of ndarray and None, optional. A location into ... ,the result of iterating j over range(N_i) , cumulatively applying ufunc to each a[k_0, ..,k_i-1}, j, . For a one-dimensional array, reduce produces results equivalent ... ,numpy.ufunc.reduce. Reduces a's dimension by one, by applying ufunc along one axis. For example, add.reduce() is equivalent to sum(). Axis or axes along which a reduction is performed. ,Then ufunc.reduce(a, axis=i)[k_0, ..,k_i-1}, k_i+1}, .., k_M-1}] = the result of iterating j over range(N_i), cumulatively applying ufunc to each a[k_0, ..,k_i-1}, j, ... ,numpy.ufunc.reduce. Reduces a's dimension by one, by applying ufunc along one axis. For example, add.reduce() is equivalent to sum(). Axis or axes along which a reduction is performed. , X = np.asarray([[1.0, 0.0, 3.0, 8.0], [2.0, 0.0, 1.0, 1.0], [1.5, 2.5, 1.0, 0.0]]) n = 4 M = np.logical_and.reduce(np.mod(X, 1) == 0, axis=-1) print(M) M ...
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 numpy logical_and reduce 相關參考資料
Numpy `logical_or` for more than two arguments - Stack Overflow
https://stackoverflow.com numpy.logical_and — NumPy v1.13 Manual
numpy. logical_and (x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'logical_and'>¶. https://docs.scipy.org numpy.logical_and — NumPy v1.14 Manual
numpy. logical_and (x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'logical_and'>¶. https://docs.scipy.org numpy.logical_and — NumPy v1.16 Manual
Parameters: x1, x2 : array_like. Input arrays. x1 and x2 must be of the same shape. out : ndarray, None, or tuple of ndarray and None, optional. A location into ... https://docs.scipy.org numpy.ufunc.reduce — NumPy v1.13 Manual
the result of iterating j over range(N_i) , cumulatively applying ufunc to each a[k_0, ..,k_i-1}, j, . For a one-dimensional array, reduce produces results equivalent ... https://docs.scipy.org numpy.ufunc.reduce — NumPy v1.14 Manual - Numpy and Scipy ...
numpy.ufunc.reduce. Reduces a's dimension by one, by applying ufunc along one axis. For example, add.reduce() is equivalent to sum(). Axis or axes along which a reduction is performed. https://docs.scipy.org numpy.ufunc.reduce — NumPy v1.15 Manual
Then ufunc.reduce(a, axis=i)[k_0, ..,k_i-1}, k_i+1}, .., k_M-1}] = the result of iterating j over range(N_i), cumulatively applying ufunc to each a[k_0, ..,k_i-1}, j, ... https://docs.scipy.org numpy.ufunc.reduce — NumPy v1.16 Manual
numpy.ufunc.reduce. Reduces a's dimension by one, by applying ufunc along one axis. For example, add.reduce() is equivalent to sum(). Axis or axes along which a reduction is performed. https://docs.scipy.org The sample of numpy.logical_and.reduce and array indexing - Qiita
X = np.asarray([[1.0, 0.0, 3.0, 8.0], [2.0, 0.0, 1.0, 1.0], [1.5, 2.5, 1.0, 0.0]]) n = 4 M = np.logical_and.reduce(np.mod(X, 1) == 0, axis=-1) print(M) M ... https://qiita.com |