import plotly.plotly as py import plotly.graph_objs as go import numpy as np
Random and Rand¶
np.random.rand() are identical functions used to return numbers sampled uniformly from the half-open interval $[0, 1)$.
np.random.rand() is just a
convenience function which is an instance of a subclass of the class
gdb.Function. By default the argument parameter
size is set to
None which means that a single random number is returned.
size can be entered as a shape, which is just a tuple of integers representing the dimentions of the array to be outputted.
import plotly.plotly as py import plotly.graph_objs as go num_of_points = 20 random_array = np.random.random((num_of_points)) trace1 = go.Scatter( x=[j for j in range(len(random_array))], y=random_array, mode='markers', marker = dict( size=15, color=random_array, colorscale='Viridis' ), name='random array' ) py.iplot([trace1], filename='numpy-random')
Help on built-in function random_sample: random_sample(...) random_sample(size=None) Return random floats in the half-open interval [0.0, 1.0). Results are from the "continuous uniform" distribution over the stated interval. To sample :math:`Unif[a, b), b > a` multiply the output of `random_sample` by `(b-a)` and add `a`:: (b - a) * random_sample() + a Parameters ---------- size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. Default is None, in which case a single value is returned. Returns ------- out : float or ndarray of floats Array of random floats of shape `size` (unless ``size=None``, in which case a single float is returned). Examples -------- >>> np.random.random_sample() 0.47108547995356098 >>> type(np.random.random_sample()) <type 'float'> >>> np.random.random_sample((5,)) array([ 0.30220482, 0.86820401, 0.1654503 , 0.11659149, 0.54323428]) Three-by-two array of random numbers from [-5, 0): >>> 5 * np.random.random_sample((3, 2)) - 5 array([[-3.99149989, -0.52338984], [-2.99091858, -0.79479508], [-1.23204345, -1.75224494]])