def one_row_rbf_kernel(X, i, gamma=None):
    '\n    X : array of shape (n_samples_X, n_features)\n    i : target sample in X (X[i])\n    gamma : float, default None\n        If None, defaults to 1.0 / n_samples_X\n    K(x, y) = exp(-gamma ||x-xi||^2)\n    Returns\n    -------\n    kernel_matrix : array of shape (n_samples_X, n_samples_Y)\n    '
    if (gamma is None):
        gamma = (1.0 / X.shape[0])
    d = np.sum(np.power((X - X[i]), 2), axis=1)
    return np.array(np.exp(((- gamma) * d)))
X : array of shape (n_samples_X, n_features)
i : target sample in X (X[i])
gamma : float, default None
    If None, defaults to 1.0 / n_samples_X
K(x, y) = exp(-gamma ||x-xi||^2)
Returns
-------
kernel_matrix : array of shape (n_samples_X, n_samples_Y)
def one_row_rbf_kernel(X, i, gamma=None):
    '\n    X : array of shape (n_samples_X, n_features)\n    i : target sample in X (X[i])\n    gamma : float, default None\n        If None, defaults to 1.0 / n_samples_X\n    K(x, y) = exp(-gamma ||x-xi||^2)\n    Returns\n    -------\n    kernel_matrix : array of shape (n_samples_X, n_samples_Y)\n    '
    if (gamma is None):
        gamma = (1.0 / X.shape[0])
    d = np.sum(np.power((X - X[i]), 2), axis=1)
    return np.array(np.exp(((- gamma) * d)))