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Diffstat (limited to 'numpy/polynomial/polynomial.py')
| -rw-r--r-- | numpy/polynomial/polynomial.py | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/numpy/polynomial/polynomial.py b/numpy/polynomial/polynomial.py index 2fead88ab..3c2663b6c 100644 --- a/numpy/polynomial/polynomial.py +++ b/numpy/polynomial/polynomial.py @@ -1304,12 +1304,12 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None): The solution is the coefficients of the polynomial `p` that minimizes the sum of the weighted squared errors - .. math :: E = \\sum_j w_j^2 * |y_j - p(x_j)|^2, + .. math:: E = \\sum_j w_j^2 * |y_j - p(x_j)|^2, where the :math:`w_j` are the weights. This problem is solved by setting up the (typically) over-determined matrix equation: - .. math :: V(x) * c = w * y, + .. math:: V(x) * c = w * y, where `V` is the weighted pseudo Vandermonde matrix of `x`, `c` are the coefficients to be solved for, `w` are the weights, and `y` are the |