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：
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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
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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：
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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。
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原文链接：https://blog.csdn.net/wzk4869/article/details/129117675
