【Python】sklearn 中的 make_blobs() 函数详解

小鱼儿 2023-09-27 14:42 143阅读 0赞

#### 文章目录 ####

*  一、介绍
 *  二、函数的使用

## 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。