Python numpy.polynomial.polynomial.polyroots() Examples

The following are 16 code examples of numpy.polynomial.polynomial.polyroots(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module numpy.polynomial.polynomial , or try the search function .
Example #1
Source File: test_polynomial.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def test_polyroots(self):
        assert_almost_equal(poly.polyroots([1]), [])
        assert_almost_equal(poly.polyroots([1, 2]), [-.5])
        for i in range(2, 5):
            tgt = np.linspace(-1, 1, i)
            res = poly.polyroots(poly.polyfromroots(tgt))
            assert_almost_equal(trim(res), trim(tgt))
Example #2
Source File: test_polynomial.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
def test_polyroots(self):
        assert_almost_equal(poly.polyroots([1]), [])
        assert_almost_equal(poly.polyroots([1, 2]), [-.5])
        for i in range(2, 5):
            tgt = np.linspace(-1, 1, i)
            res = poly.polyroots(poly.polyfromroots(tgt))
            assert_almost_equal(trim(res), trim(tgt))
Example #3
Source File: test_polynomial.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
def test_polyroots(self):
        assert_almost_equal(poly.polyroots([1]), [])
        assert_almost_equal(poly.polyroots([1, 2]), [-.5])
        for i in range(2, 5):
            tgt = np.linspace(-1, 1, i)
            res = poly.polyroots(poly.polyfromroots(tgt))
            assert_almost_equal(trim(res), trim(tgt))
Example #4
Source File: test_polynomial.py    From Computable with MIT License 5 votes vote down vote up 
def test_polyroots(self) :
        assert_almost_equal(poly.polyroots([1]), [])
        assert_almost_equal(poly.polyroots([1, 2]), [-.5])
        for i in range(2, 5) :
            tgt = np.linspace(-1, 1, i)
            res = poly.polyroots(poly.polyfromroots(tgt))
            assert_almost_equal(trim(res), trim(tgt))
Example #5
Source File: test_polynomial.py    From Mastering-Elasticsearch-7.0 with MIT License 5 votes vote down vote up 
def test_polyroots(self):
        assert_almost_equal(poly.polyroots([1]), [])
        assert_almost_equal(poly.polyroots([1, 2]), [-.5])
        for i in range(2, 5):
            tgt = np.linspace(-1, 1, i)
            res = poly.polyroots(poly.polyfromroots(tgt))
            assert_almost_equal(trim(res), trim(tgt))
Example #6
Source File: test_polynomial.py    From GraphicDesignPatternByPython with MIT License 5 votes vote down vote up 
def test_polyroots(self):
        assert_almost_equal(poly.polyroots([1]), [])
        assert_almost_equal(poly.polyroots([1, 2]), [-.5])
        for i in range(2, 5):
            tgt = np.linspace(-1, 1, i)
            res = poly.polyroots(poly.polyfromroots(tgt))
            assert_almost_equal(trim(res), trim(tgt))
Example #7
Source File: test_polynomial.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def test_polyroots(self):
        assert_almost_equal(poly.polyroots([1]), [])
        assert_almost_equal(poly.polyroots([1, 2]), [-.5])
        for i in range(2, 5):
            tgt = np.linspace(-1, 1, i)
            res = poly.polyroots(poly.polyfromroots(tgt))
            assert_almost_equal(trim(res), trim(tgt))
Example #8
Source File: test_polynomial.py    From pySINDy with MIT License 5 votes vote down vote up 
def test_polyroots(self):
        assert_almost_equal(poly.polyroots([1]), [])
        assert_almost_equal(poly.polyroots([1, 2]), [-.5])
        for i in range(2, 5):
            tgt = np.linspace(-1, 1, i)
            res = poly.polyroots(poly.polyfromroots(tgt))
            assert_almost_equal(trim(res), trim(tgt))
Example #9
Source File: test_polynomial.py    From mxnet-lambda with Apache License 2.0 5 votes vote down vote up 
def test_polyroots(self):
        assert_almost_equal(poly.polyroots([1]), [])
        assert_almost_equal(poly.polyroots([1, 2]), [-.5])
        for i in range(2, 5):
            tgt = np.linspace(-1, 1, i)
            res = poly.polyroots(poly.polyfromroots(tgt))
            assert_almost_equal(trim(res), trim(tgt))
Example #10
Source File: test_polynomial.py    From ImageFusion with MIT License 5 votes vote down vote up 
def test_polyroots(self):
        assert_almost_equal(poly.polyroots([1]), [])
        assert_almost_equal(poly.polyroots([1, 2]), [-.5])
        for i in range(2, 5):
            tgt = np.linspace(-1, 1, i)
            res = poly.polyroots(poly.polyfromroots(tgt))
            assert_almost_equal(trim(res), trim(tgt))
Example #11
Source File: test_polynomial.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up 
def test_polyroots(self):
        assert_almost_equal(poly.polyroots([1]), [])
        assert_almost_equal(poly.polyroots([1, 2]), [-.5])
        for i in range(2, 5):
            tgt = np.linspace(-1, 1, i)
            res = poly.polyroots(poly.polyfromroots(tgt))
            assert_almost_equal(trim(res), trim(tgt))
Example #12
Source File: test_polynomial.py    From coffeegrindsize with MIT License 5 votes vote down vote up 
def test_polyroots(self):
        assert_almost_equal(poly.polyroots([1]), [])
        assert_almost_equal(poly.polyroots([1, 2]), [-.5])
        for i in range(2, 5):
            tgt = np.linspace(-1, 1, i)
            res = poly.polyroots(poly.polyfromroots(tgt))
            assert_almost_equal(trim(res), trim(tgt))
Example #13
Source File: test_polynomial.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 5 votes vote down vote up 
def test_polyroots(self):
        assert_almost_equal(poly.polyroots([1]), [])
        assert_almost_equal(poly.polyroots([1, 2]), [-.5])
        for i in range(2, 5):
            tgt = np.linspace(-1, 1, i)
            res = poly.polyroots(poly.polyfromroots(tgt))
            assert_almost_equal(trim(res), trim(tgt))
Example #14
Source File: test_polynomial.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
def test_polyroots(self):
        assert_almost_equal(poly.polyroots([1]), [])
        assert_almost_equal(poly.polyroots([1, 2]), [-.5])
        for i in range(2, 5):
            tgt = np.linspace(-1, 1, i)
            res = poly.polyroots(poly.polyfromroots(tgt))
            assert_almost_equal(trim(res), trim(tgt))
Example #15
Source File: test_polynomial.py    From keras-lambda with MIT License 5 votes vote down vote up 
def test_polyroots(self):
        assert_almost_equal(poly.polyroots([1]), [])
        assert_almost_equal(poly.polyroots([1, 2]), [-.5])
        for i in range(2, 5):
            tgt = np.linspace(-1, 1, i)
            res = poly.polyroots(poly.polyfromroots(tgt))
            assert_almost_equal(trim(res), trim(tgt))
Example #16
Source File: prony_decomposition.py    From capytaine with GNU General Public License v3.0 4 votes vote down vote up 
def exponential_decomposition(X, F, m):
    """Use Prony's method to approximate the sampled real function F=f(X) as a sum of m
    exponential functions x → Σ a_i exp(lamda_i x).

    Parameters
    ----------
    X: 1D array
        sampling points.
    F: 1D array (same size as X)
        values of the function to approximate at the points of x.
    m: integer
        number of exponential functions

    Return
    ------
    a: 1D array (size m)
        coefficients of the exponentials
    lamda: 1D array (size m)
        growth rate of the exponentials
    """
    assert X.shape == F.shape

    # Compute the coefficients of the polynomials of Prony's method
    A = toeplitz(c=F[m-1:-1], r=F[:m][::-1])
    P, *_ = np.linalg.lstsq(A, F[m:], rcond=None)

    # Build and solve polynomial function
    coeffs = np.ones(m+1)
    # coeffs[:m] = -P[::-1]
    for i in range(m):
        coeffs[m-i-1] = -P[i]
    roots = polynomial.polyroots(coeffs)

    # Discard values where log is undefined
    roots = roots[np.logical_or(np.imag(roots) != 0.0, np.real(roots) >= 0.0)]

    # Deduce lamda and keep only interesting values
    lamda = np.real(np.log(roots)/(X[1] - X[0]))
    lamda = np.unique(lamda)
    lamda = lamda[np.logical_and(-20.0 < lamda, lamda < 0.0)]

    # Fit the values of 'a' on the curve
    def f(x, *ar):
        ar = np.asarray(ar)[:, np.newaxis]
        la = lamda[:, np.newaxis]
        return np.sum(ar * np.exp(la * x), axis=0)
    a, *_ = curve_fit(f, X, F, p0=np.zeros(lamda.shape))

    return a, lamda