Python numpy.ones方法代碼示例

# 需要導入模塊: from autograd import numpy [as 別名]
# 或者: from autograd.numpy import ones [as 別名]
def expected_tmrca(demography, sampled_pops=None, sampled_n=None):
    """
    The expected time to most recent common ancestor of the sample.

    Parameters
    ----------
    demography : Demography

    Returns
    -------
    tmrca : float-like

    See Also
    --------
    expected_deme_tmrca : tmrca of subsample within a deme
    expected_sfs_tensor_prod : compute general class of summary statistics
    """
    vecs = [np.ones(n + 1) for n in demography.sampled_n]
    n0 = len(vecs[0]) - 1.0
    vecs[0] = np.arange(n0 + 1) / n0
    return np.squeeze(expected_sfs_tensor_prod(vecs, demography)) 

# 需要導入模塊: from autograd import numpy [as 別名]
# 或者: from autograd.numpy import ones [as 別名]
def _mut_factor_het(sfs, demo, mut_rate, vector, p_missing):
    mut_rate = mut_rate * np.ones(sfs.n_loci)
    E_het = expected_heterozygosity(
        demo,
        restrict_to_pops=np.array(
            sfs.sampled_pops)[sfs.ascertainment_pop])

    p_missing = p_missing * np.ones(len(sfs.ascertainment_pop))
    p_missing = p_missing[sfs.ascertainment_pop]
    lambd = np.einsum("i,j->ij", mut_rate, E_het * (1.0 - p_missing))

    counts = sfs.avg_pairwise_hets[:, sfs.ascertainment_pop]
    ret = -lambd + counts * np.log(lambd) - scipy.special.gammaln(counts + 1)
    ret = ret * sfs.sampled_n[sfs.ascertainment_pop] / float(
        np.sum(sfs.sampled_n[sfs.ascertainment_pop]))
    if not vector:
        ret = np.sum(ret)
    else:
        ret = np.sum(ret, axis=1)
    return ret 

# 需要導入模塊: from autograd import numpy [as 別名]
# 或者: from autograd.numpy import ones [as 別名]
def get_mcl_normal_direction_at_chord_fraction(self, chord_fraction):
        # Returns the normal direction of the mean camber line at a specified chord fraction.
        # If you input a single value, returns a 1D numpy array with 2 elements (x,y).
        # If you input a vector of values, returns a 2D numpy array. First index is the point number, second index is (x,y)

        # Right now, does it by finite differencing camber values :(
        # When I'm less lazy I'll make it do it in a proper, more efficient way
        # TODO make this not finite difference
        epsilon = np.sqrt(np.finfo(float).eps)

        cambers = self.get_camber_at_chord_fraction(chord_fraction)
        cambers_incremented = self.get_camber_at_chord_fraction(chord_fraction + epsilon)
        dydx = (cambers_incremented - cambers) / epsilon

        if dydx.shape == 1:  # single point
            normal = np.hstack((-dydx, 1))
            normal /= np.linalg.norm(normal)
            return normal
        else:  # multiple points vectorized
            normal = np.column_stack((-dydx, np.ones(dydx.shape)))
            normal /= np.expand_dims(np.linalg.norm(normal, axis=1), axis=1)  # normalize
            return normal 

# 需要導入模塊: from autograd import numpy [as 別名]
# 或者: from autograd.numpy import ones [as 別名]
def optimize_and_lls(optfun):
        num_iters = 200
        elbos     = []
        def callback(params, t, g):
            elbo_val = -objective(params, t)
            elbos.append(elbo_val)
            if t % 50 == 0:
                print("Iteration {} lower bound {}".format(t, elbo_val))

        init_mean    = -1 * np.ones(D)
        init_log_std = -5 * np.ones(D)
        init_var_params = np.concatenate([init_mean, init_log_std])
        variational_params = optfun(num_iters, init_var_params, callback)
        return np.array(elbos)

    # let's optimize this with a few different step sizes 

# 需要導入模塊: from autograd import numpy [as 別名]
# 或者: from autograd.numpy import ones [as 別名]
def test_getter():
    def fun(input_list):
        A = np.sum(input_list[0])
        B = np.sum(input_list[1])
        C = np.sum(input_list[1])
        return A + B + C

    d_fun = grad(fun)
    input_list = [npr.randn(5, 6),
                   npr.randn(4, 3),
                   npr.randn(2, 4)]

    result = d_fun(input_list)
    assert np.allclose(result[0], np.ones((5, 6)))
    assert np.allclose(result[1], 2 * np.ones((4, 3)))
    assert np.allclose(result[2], np.zeros((2, 4))) 

# 需要導入模塊: from autograd import numpy [as 別名]
# 或者: from autograd.numpy import ones [as 別名]
def test_getter():
    def fun(input_tuple):
        A = np.sum(input_tuple[0])
        B = np.sum(input_tuple[1])
        C = np.sum(input_tuple[1])
        return A + B + C

    d_fun = grad(fun)
    input_tuple = (npr.randn(5, 6),
                   npr.randn(4, 3),
                   npr.randn(2, 4))

    result = d_fun(input_tuple)
    assert np.allclose(result[0], np.ones((5, 6)))
    assert np.allclose(result[1], 2 * np.ones((4, 3)))
    assert np.allclose(result[2], np.zeros((2, 4))) 

# 需要導入模塊: from autograd import numpy [as 別名]
# 或者: from autograd.numpy import ones [as 別名]
def test_getter():
    def fun(input_dict):
        A = np.sum(input_dict['item_1'])
        B = np.sum(input_dict['item_2'])
        C = np.sum(input_dict['item_2'])
        return A + B + C

    d_fun = grad(fun)
    input_dict = {'item_1' : npr.randn(5, 6),
                  'item_2' : npr.randn(4, 3),
                  'item_X' : npr.randn(2, 4)}

    result = d_fun(input_dict)
    assert np.allclose(result['item_1'], np.ones((5, 6)))
    assert np.allclose(result['item_2'], 2 * np.ones((4, 3)))
    assert np.allclose(result['item_X'], np.zeros((2, 4))) 

# 需要導入模塊: from autograd import numpy [as 別名]
# 或者: from autograd.numpy import ones [as 別名]
def make_IO_matrices(indices, N):
    """ Makes matrices that relate the sparse matrix entries to their locations in the matrix
            The kth column of I is a 'one hot' vector specifing the k-th entries row index into A
            The kth column of J is a 'one hot' vector specifing the k-th entries columnn index into A
            O = J^T is for notational convenience.
            Armed with a vector of M entries 'a', we can construct the sparse matrix 'A' as:
                A = I @ diag(a) @ O
            where 'diag(a)' is a (MxM) matrix with vector 'a' along its diagonal.
            In index notation:
                A_ij = I_ik * a_k * O_kj
            In an opposite way, given sparse matrix 'A' we can strip out the entries `a` using the IO matrices as follows:
                a = diag(I^T @ A @ O^T)
            In index notation:
                a_k = I_ik * A_ij * O_kj
    """
    M = indices.shape[1]                                 # number of indices in the matrix
    entries_1 = npa.ones(M)                              # M entries of all 1's
    ik, jk = indices                                     # separate i and j components of the indices
    indices_I = npa.vstack((ik, npa.arange(M)))          # indices into the I matrix
    indices_J = npa.vstack((jk, npa.arange(M)))          # indices into the J matrix
    I = make_sparse(entries_1, indices_I, shape=(N, M))  # construct the I matrix
    J = make_sparse(entries_1, indices_J, shape=(N, M))  # construct the J matrix
    O = J.T                                              # make O = J^T matrix for consistency with my notes.
    return I, O 

# 需要導入模塊: from autograd import numpy [as 別名]
# 或者: from autograd.numpy import ones [as 別名]
def _make_A(self, eps_vec):

        eps_vec_xx, eps_vec_yy = self._grid_average_2d(eps_vec)
        eps_vec_xx_inv = 1 / (eps_vec_xx + 1e-5)  # the 1e-5 is for numerical stability
        eps_vec_yy_inv = 1 / (eps_vec_yy + 1e-5)  # autograd throws 'divide by zero' errors.

        indices_diag = npa.vstack((npa.arange(self.N), npa.arange(self.N)))

        entries_DxEpsy,   indices_DxEpsy   = spsp_mult(self.entries_Dxb, self.indices_Dxb, eps_vec_yy_inv, indices_diag, self.N)
        entires_DxEpsyDx, indices_DxEpsyDx = spsp_mult(entries_DxEpsy, indices_DxEpsy, self.entries_Dxf, self.indices_Dxf, self.N)

        entries_DyEpsx,   indices_DyEpsx   = spsp_mult(self.entries_Dyb, self.indices_Dyb, eps_vec_xx_inv, indices_diag, self.N)
        entires_DyEpsxDy, indices_DyEpsxDy = spsp_mult(entries_DyEpsx, indices_DyEpsx, self.entries_Dyf, self.indices_Dyf, self.N)

        entries_d = 1 / EPSILON_0 * npa.hstack((entires_DxEpsyDx, entires_DyEpsxDy))
        indices_d = npa.hstack((indices_DxEpsyDx, indices_DyEpsxDy))

        entries_diag = MU_0 * self.omega**2 * npa.ones(self.N)

        entries_a = npa.hstack((entries_d, entries_diag))
        indices_a = npa.hstack((indices_d, indices_diag))

        return entries_a, indices_a 

# 需要導入模塊: from autograd import numpy [as 別名]
# 或者: from autograd.numpy import ones [as 別名]
def test_preconditioning():
    def f(x, y, z, a, b):
        return np.sum(x ** 2) + np.sum((y - 3) ** 2) + np.sum((z + a) ** 4)

    a = 2
    b = 5
    shapes = [(2, 3), (2, 2), (3,)]
    optim_vars_init = [np.ones(shape) for shape in shapes]

    def precon_fwd(x, y, z, a, b):
        return 3 * x, y / 2, z * 4

    def precon_bwd(x, y, z, a, b):
        return x / 3, 2 * y, z / 4

    optim_vars, res = minimize(f, optim_vars_init, args=(a, b),
                               precon_fwd=precon_fwd, precon_bwd=precon_bwd)
    assert res['success']
    assert [var.shape for var in optim_vars] == shapes
    for var, target in zip(optim_vars, [0, 3, -a]):
        assert_allclose(var, target, atol=1e-1) 

# 需要導入模塊: from autograd import numpy [as 別名]
# 或者: from autograd.numpy import ones [as 別名]
def __init__(self, means, variances, pmix=None):
        """
        means: a k x d 2d array specifying the means.
        variances: a one-dimensional length-k array of variances
        pmix: a one-dimensional length-k array of mixture weights. Sum to one.
        """
        k, d = means.shape
        if k != len(variances):
            raise ValueError('Number of components in means and variances do not match.')

        if pmix is None:
            pmix = old_div(np.ones(k),float(k))

        if np.abs(np.sum(pmix) - 1) > 1e-8:
            raise ValueError('Mixture weights do not sum to 1.')

        self.pmix = pmix
        self.means = means
        self.variances = variances 

# 需要導入模塊: from autograd import numpy [as 別名]
# 或者: from autograd.numpy import ones [as 別名]
def constraint_c2(f, r):
    n_obj = f.shape[1]

    v1 = anp.inf * anp.ones(f.shape[0])

    for i in range(n_obj):
        temp = (f[:, i] - 1) ** 2 + (anp.sum(f ** 2, axis=1) - f[:, i] ** 2) - r ** 2
        v1 = anp.minimum(temp.flatten(), v1)

    a = 1 / anp.sqrt(n_obj)
    v2 = anp.sum((f - a) ** 2, axis=1) - r ** 2
    g = anp.minimum(v1, v2.flatten())

    return g 

# 需要導入模塊: from autograd import numpy [as 別名]
# 或者: from autograd.numpy import ones [as 別名]
def __init__(self):
        self.n_var = 20
        self.n_constr = 2
        self.n_obj = 1
        self.func = self._evaluate
        self.xl = anp.zeros(self.n_var)
        self.xu = 10 * anp.ones(self.n_var)
        super(G2, self).__init__(n_var=self.n_var, n_obj=self.n_obj, n_constr=self.n_constr, xl=self.xl, xu=self.xu,
                                 type_var=anp.double) 

# 需要導入模塊: from autograd import numpy [as 別名]
# 或者: from autograd.numpy import ones [as 別名]
def __init__(self, n_var=10):
        super().__init__(n_var)
        self.xl = -5 * anp.ones(self.n_var)
        self.xl[0] = 0.0
        self.xu = 5 * anp.ones(self.n_var)
        self.xu[0] = 1.0
        self.func = self._evaluate 

# 需要導入模塊: from autograd import numpy [as 別名]
# 或者: from autograd.numpy import ones [as 別名]
def __init__(self, const_1=5, const_2=0.1):

        # define lower and upper bounds -  1d array with length equal to number of variable
        xl = -5 * anp.ones(10)
        xu = 5 * anp.ones(10)

        super().__init__(n_var=10, n_obj=1, n_constr=2, xl=xl, xu=xu, evaluation_of="auto")

        # store custom variables needed for evaluation
        self.const_1 = const_1
        self.const_2 = const_2

    # implemented the function evaluation function - the arrays to fill are provided directly