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# Random

How to sample numbers uniformly between 0 and 1.

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### Imports¶

This tutorial imports Numpy.

In [1]:
import plotly.plotly as py
import plotly.graph_objs as go

import numpy as np


#### Random and Rand¶

np.random.random() and 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.

In [2]:
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')

Out[2]:
In [2]:
help(np.random.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]])


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