diff options
Diffstat (limited to 'numpy/lib/twodim_base.py')
| -rw-r--r-- | numpy/lib/twodim_base.py | 52 |
1 files changed, 27 insertions, 25 deletions
diff --git a/numpy/lib/twodim_base.py b/numpy/lib/twodim_base.py index 20b5cdd67..4c634921d 100644 --- a/numpy/lib/twodim_base.py +++ b/numpy/lib/twodim_base.py @@ -11,7 +11,8 @@ __all__ = ['diag', 'diagflat', 'eye', 'fliplr', 'flipud', 'rot90', 'tri', from numpy.core.numeric import ( asanyarray, subtract, arange, zeros, greater_equal, multiply, ones, - asarray, where, dtype as np_dtype, less, int8, int16, int32, int64 + asarray, where, dtype as np_dtype, less, int8, int16, int32, int64, + empty, promote_types ) from numpy.core import iinfo @@ -483,17 +484,16 @@ def triu(m, k=0): # Originally borrowed from John Hunter and matplotlib -def vander(x, N=None, order='decreasing'): +def vander(x, N=None, increasing=False): """ Generate a Vandermonde matrix. - The columns of the output matrix are powers of the input vector. The - order of the powers is determined by the `order` argument, either - "decreasing" (the default) or "increasing". Specifically, when - `order` is "decreasing", the `i`-th output column is the input vector - raised element-wise to the power of ``N - i - 1``. Such a matrix with - a geometric progression in each row is named for Alexandre-Theophile - Vandermonde. + The columns of the output matrix are powers of the input vector. The + order of the powers is determined by the `increasing` boolean argument. + Specifically, when `increasing` is False, the `i`-th output column is + the input vector raised element-wise to the power of ``N - i - 1``. Such + a matrix with a geometric progression in each row is named for Alexandre- + Theophile Vandermonde. Parameters ---------- @@ -502,16 +502,18 @@ def vander(x, N=None, order='decreasing'): N : int, optional Number of columns in the output. If `N` is not specified, a square array is returned (``N = len(x)``). - order : str, optional - Order of the powers of the columns. Must be either 'decreasing' - (the default) or 'increasing'. + increasing : bool, optional + Order of the powers of the columns. If True, the powers increase + from left to right, if False (the default) they are reversed. + + .. versionadded:: 1.9.0 Returns ------- out : ndarray - Vandermonde matrix. If `order` is "decreasing", the first column is - ``x^(N-1)``, the second ``x^(N-2)`` and so forth. If `order` is - "increasing", the columns are ``x^0, x^1, ..., x^(N-1)``. + Vandermonde matrix. If `increasing` is False, the first column is + ``x^(N-1)``, the second ``x^(N-2)`` and so forth. If `increasing` is + True, the columns are ``x^0, x^1, ..., x^(N-1)``. See Also -------- @@ -539,7 +541,7 @@ def vander(x, N=None, order='decreasing'): [ 8, 4, 2, 1], [ 27, 9, 3, 1], [125, 25, 5, 1]]) - >>> np.vander(x, order='increasing') + >>> np.vander(x, increasing=True) array([[ 1, 1, 1, 1], [ 1, 2, 4, 8], [ 1, 3, 9, 27], @@ -554,22 +556,22 @@ def vander(x, N=None, order='decreasing'): 48 """ - if order not in ['decreasing', 'increasing']: - raise ValueError("Invalid order %r; order must be either " - "'decreasing' or 'increasing'." % (order,)) x = asarray(x) if x.ndim != 1: raise ValueError("x must be a one-dimensional array or sequence.") if N is None: N = len(x) - if order == "decreasing": - powers = arange(N - 1, -1, -1) - else: - powers = arange(N) - V = x.reshape(-1, 1) ** powers + v = empty((len(x), N), dtype=promote_types(x.dtype, int)) + tmp = v[:, ::-1] if not increasing else v + + if N > 0: + tmp[:, 0] = 1 + if N > 1: + tmp[:, 1:] = x[:, None] + multiply.accumulate(tmp[:, 1:], out=tmp[:, 1:], axis=1) - return V + return v def histogram2d(x, y, bins=10, range=None, normed=False, weights=None): |