- 昨日の記事は、scikit-learnをべたにやろうかと思ったその書きかけ
- しばらくやったら、たくさんありすぎることと、まあ、コピペすれば動くじゃない！ということでやる気がなくなる
- が、そうは言っても手を動かすことは慣れるために必要
- ただし、Exampleは読みにくいものも多く、なんだかなーと思わないでもない
- ということで、関数に出会ったら、その書かれ方を読むのが手っ取り早いだろう、ということで
from sklearn.datasets.samples_generator import make_blobs
import inspect
inspect.getsource(make_blobs)
print(inspect.getsource(make_blobs))
- 逆に、関数定義を書くときは、このinspect.getsource()で書きださせたときに、諸情報が現れるように、冒頭に、コメント欄を指定 (""","""でサンドイッチ)すればよさそうだ
- そのコメント欄には、
- 簡易説明
- Parameters
- Returns
- Exanmples
- See also
- があればよいだろうか？他にも書式として定形的なものがあるだろうか？
import inspect
inspect.getsource(make_blobs)
Out[154]: 'def make_blobs(n_samples=100, n_features=2, centers=3, cluster_std=1.0,\n center_box=(-10.0, 10.0), shuffle=True, random_state=None):\n """Generate isotropic Gaussian blobs for clustering.\n\n Read more in the :ref:`User Guide <sample_generators>`.\n\n Parameters\n ----------\n n_samples : int, optional (default=100)\n The total number of points equally divided among clusters.\n\n n_features : int, optional (default=2)\n The number of features for each sample.\n\n centers : int or array of shape [n_centers, n_features], optional\n (default=3)\n The number of centers to generate, or the fixed center locations.\n\n cluster_std: float or sequence of floats, optional (default=1.0)\n The standard deviation of the clusters.\n\n center_box: pair of floats (min, max), optional (default=(-10.0, 10.0))\n The bounding box for each cluster center when centers are\n generated at random.\n\n shuffle : boolean, optional (default=True)\n Shuffle the samples.\n\n random_state : int, RandomState instance or None, optional (default=None)\n If int, random_state is the seed used by the random number generator;\n If RandomState instance, random_state is the random number generator;\n If None, the random number generator is the RandomState instance used\n by `np.random`.\n\n Returns\n -------\n X : array of shape [n_samples, n_features]\n The generated samples.\n\n y : array of shape [n_samples]\n The integer labels for cluster membership of each sample.\n\n Examples\n --------\n >>> from sklearn.datasets.samples_generator import make_blobs\n >>> X, y = make_blobs(n_samples=10, centers=3, n_features=2,\n ... random_state=0)\n >>> print(X.shape)\n (10, 2)\n >>> y\n array([0, 0, 1, 0, 2, 2, 2, 1, 1, 0])\n\n See also\n --------\n make_classification: a more intricate variant\n """\n generator = check_random_state(random_state)\n\n if isinstance(centers, numbers.Integral):\n centers = generator.uniform(center_box[0], center_box[1],\n size=(centers, n_features))\n else:\n centers = check_array(centers)\n n_features = centers.shape[1]\n\n if isinstance(cluster_std, numbers.Real):\n cluster_std = np.ones(len(centers)) * cluster_std\n\n X = []\n y = []\n\n n_centers = centers.shape[0]\n n_samples_per_center = [int(n_samples // n_centers)] * n_centers\n\n for i in range(n_samples % n_centers):\n n_samples_per_center[i] += 1\n\n for i, (n, std) in enumerate(zip(n_samples_per_center, cluster_std)):\n X.append(centers[i] + generator.normal(scale=std,\n size=(n, n_features)))\n y += [i] * n\n\n X = np.concatenate(X)\n y = np.array(y)\n\n if shuffle:\n indices = np.arange(n_samples)\n generator.shuffle(indices)\n X = X[indices]\n y = y[indices]\n\n return X, y\n'
print(inspect.getsource(make_blobs))
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
