Python kernel_normの例

コード例 #1

ファイル: test_kde.py プロジェクト: BasilBeirouti/scikit-learn

def compute_kernel_slow(Y, X, kernel, h):
    d = np.sqrt(((Y[:, None, :] - X) ** 2).sum(-1))
    norm = kernel_norm(h, X.shape[1], kernel) / X.shape[0]

    if kernel == 'gaussian':
        return norm * np.exp(-0.5 * (d * d) / (h * h)).sum(-1)
    elif kernel == 'tophat':
        return norm * (d < h).sum(-1)
    elif kernel == 'epanechnikov':
        return norm * ((1.0 - (d * d) / (h * h)) * (d < h)).sum(-1)
    elif kernel == 'exponential':
        return norm * (np.exp(-d / h)).sum(-1)
    elif kernel == 'linear':
        return norm * ((1 - d / h) * (d < h)).sum(-1)
    elif kernel == 'cosine':
        return norm * (np.cos(0.5 * np.pi * d / h) * (d < h)).sum(-1)
    else:
        raise ValueError('kernel not recognized')

コード例 #2

ファイル: test_kde.py プロジェクト: COGUS210/traffic_accident_Project

def compute_kernel_slow(Y, X, kernel, h):
    d = np.sqrt(((Y[:, None, :] - X)**2).sum(-1))
    norm = kernel_norm(h, X.shape[1], kernel) / X.shape[0]

    if kernel == 'gaussian':
        return norm * np.exp(-0.5 * (d * d) / (h * h)).sum(-1)
    elif kernel == 'tophat':
        return norm * (d < h).sum(-1)
    elif kernel == 'epanechnikov':
        return norm * ((1.0 - (d * d) / (h * h)) * (d < h)).sum(-1)
    elif kernel == 'exponential':
        return norm * (np.exp(-d / h)).sum(-1)
    elif kernel == 'linear':
        return norm * ((1 - d / h) * (d < h)).sum(-1)
    elif kernel == 'cosine':
        return norm * (np.cos(0.5 * np.pi * d / h) * (d < h)).sum(-1)
    else:
        raise ValueError('kernel not recognized')

コード例 #3

ファイル: test_ball_tree.py プロジェクト: albertotb/scikit-learn

def compute_kernel_slow(Y, X, kernel, h):
    d = np.sqrt(((Y[:, None, :] - X) ** 2).sum(-1))
    norm = kernel_norm(h, X.shape[1], kernel)

    if kernel == "gaussian":
        return norm * np.exp(-0.5 * (d * d) / (h * h)).sum(-1)
    elif kernel == "tophat":
        return norm * (d < h).sum(-1)
    elif kernel == "epanechnikov":
        return norm * ((1.0 - (d * d) / (h * h)) * (d < h)).sum(-1)
    elif kernel == "exponential":
        return norm * (np.exp(-d / h)).sum(-1)
    elif kernel == "linear":
        return norm * ((1 - d / h) * (d < h)).sum(-1)
    elif kernel == "cosine":
        return norm * (np.cos(0.5 * np.pi * d / h) * (d < h)).sum(-1)
    else:
        raise ValueError("kernel not recognized")

コード例 #4

ファイル: adaptive_kde.py プロジェクト: siddharthsarda/spams

def exponential_norm(h,dimension=2):
    return kernel_norm(h, dimension, "exponential")

コード例 #5

def exponential_norm(h, dimension=2):
    return kernel_norm(h, dimension, "exponential")