Numpy

bincount

Count number of occurrences of each value in array of non-negative ints.

numpy.bincount(x, weights=None, minlength=None)

https://docs.scipy.org/doc/numpy/reference/generated/numpy.bincount.html

loadtxt

numpy.loadtxt(fname, dtype=<type 'float'>, comments='#', delimiter=None, converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0)

https://docs.scipy.org/doc/numpy/reference/generated/numpy.loadtxt.html
cf. fromstring

• fromstring쓸 때, sep argument로 아무것도 넘겨주지 않으면 binary취급함에 주의. 탭구분자등은 sep=' '와 같이 공백만 주어도 된다.

histogram

numpy.histogram(a, bins=10, range=None, normed=False, weights=None, density=None)

>>> import matplotlib.pyplot as plt
>>> rng = np.random.RandomState(10)  # deterministic random data
>>> a = np.hstack((rng.normal(size=1000),
...                rng.normal(loc=5, scale=2, size=1000)))
>>> plt.hist(a, bins='auto')  # plt.hist passes it's arguments to np.histogram
>>> plt.title("Histogram with 'auto' bins")
>>> plt.show()

https://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram.html

Array to column vector

>>> a = np.array([1, 2, 3])
>>> a
array([1, 2, 3])
>>> a[:, np.newaxis]
array([[1],
       [2],
       [3]])
>>> a[np.newaxis, :]
array([[1, 2, 3]])

http://stackoverflow.com/a/17428859/766330

Get a distance matrix

scipy.spatial.distance.pdist(X, metric='euclidean', p=None, w=None, V=None, VI=None)
X : ndarray

X is m by n matrix, and rows are observations. So X is m observations.
pdist means pairwise distance. From this, scipy.spatial.distance.squareform(X) can make the distance matrix.[1]