Pythonsklearn中的make_blobs()函数示例详解

Python sklearn make_blobs()函数 Python make_blobs()函数 2023-02-22 12:02:11 165人浏览泡泡鱼

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摘要

目录一、介绍一、介绍 make_blobs() 是 sklearn.datasets中的一个函数。主要是产生聚类数据集，产生一个数据集和相应的标签。函数的源代码如下： def m

一、介绍

make_blobs() 是 sklearn.datasets中的一个函数。

主要是产生聚类数据集，产生一个数据集和相应的标签。

函数的源代码如下：

def make_blobs(n_samples = 100, n_features = 2, centers = 3, cluster_std = 1.0,
               center_box = (-10.0, 10.0), shuffle = True, random_state = None):
    """Generate isotropic Gaussian blobs for clustering.

    Read more in the :ref:`User Guide <sample_generators>`.

    Parameters
    ----------
    n_samples : int, optional (default=100)
        The total number of points equally divided among clusters.

    n_features : int, optional (default=2)
        The number of features for each sample.

    centers : int or array of shape [n_centers, n_features], optional
        (default=3)
        The number of centers to generate, or the fixed center locations.

    cluster_std: float or sequence of floats, optional (default=1.0)
        The standard deviation of the clusters.

    center_box: pair of floats (min, max), optional (default=(-10.0, 10.0))
        The bounding box for each cluster center when centers are
        generated at random.

    shuffle : boolean, optional (default=True)
        Shuffle the samples.

    random_state : int, RandomState instance or None, optional (default=None)
        If int, random_state is the seed used by the random number generator;
        If RandomState instance, random_state is the random number generator;
        If None, the random number generator is the RandomState instance used
        by `np.random`.

    Returns
    -------
    X : array of shape [n_samples, n_features]
        The generated samples.

    y : array of shape [n_samples]
        The integer labels for cluster membership of each sample.

    Examples
    --------
    >>> from sklearn.datasets.samples_generator import make_blobs
    >>> X, y = make_blobs(n_samples=10, centers=3, n_features=2,
    ...                   random_state=0)
    >>> print(X.shape)
    (10, 2)
    >>> y
    array([0, 0, 1, 0, 2, 2, 2, 1, 1, 0])

    See also
    --------
    make_classification: a more intricate variant
    """
    generator = check_random_state(random_state)

    if isinstance(centers, numbers.Integral):
        centers = generator.unifORM(center_box[0], center_box[1],
                                    size=(centers, n_features))
    else:
        centers = check_array(centers)
        n_features = centers.shape[1]

    if isinstance(cluster_std, numbers.Real):
        cluster_std = np.ones(len(centers)) * cluster_std

    X = []
    y = []

    n_centers = centers.shape[0]
    n_samples_per_center = [int(n_samples // n_centers)] * n_centers

    for i in range(n_samples % n_centers):
        n_samples_per_center[i] += 1

    for i, (n, std) in enumerate(zip(n_samples_per_center, cluster_std)):
        X.append(centers[i] + generator.normal(scale = std,
                                               size = (n, n_features)))
        y += [i] * n

    X = np.concatenate(X)
    y = np.array(y)

    if shuffle:
        indices = np.arange(n_samples)
        generator.shuffle(indices)
        X = X[indices]
        y = y[indices]

    return X, y

二、函数的使用

make_blobs(n_samples = 100, n_features = 2, centers = 3, cluster_std = 1.0, center_box = (-10.0, 10.0), shuffle = True, random_state = None)

可以看到它有 7 个参数：

n_samples = 100 ，表示数据样本点个数,默认值100；
n_features = 2 ，是每个样本的特征（或属性）数，也表示数据的维度，默认值是2；
centers = 3 ，表示类别数（标签的种类数），默认值3；
cluster_std = 1.0 ，表示每个类别的方差，例如我们希望生成2类数据，其中一类比另一类具有更大的方差，可以将cluster_std设置为[1.0, 3.0]，浮点数或者浮点数序列，默认值1.0；
center_box = (-10.0, 10.0) ，中心确定之后的数据边界，默认值(-10.0, 10.0)；
shuffle = True ，将数据进行洗乱，默认值是True；
random_state = None ，官网解释是随机生成器的种子，可以固定生成的数据，给定数之后，每次生成的数据集就是固定的。若不给定值，则由于随机性将导致每次运行程序所获得的的结果可能有所不同。在使用数据生成器练习机器学习算法练习或python练习时建议给定数值。