Interact Exercise 4


In [1]:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from random import randint

In [2]:
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display

:0: FutureWarning: IPython widgets are experimental and may change in the future.

Line with Gaussian noise

Write a function named random_line that creates x and y data for a line with y direction random noise that has a normal distribution $N(0,\sigma^2)$:

$$ y = m x + b + N(0,\sigma^2) $$

Be careful about the sigma=0.0 case.

In [10]:
def random_line(m, b, sigma, size=10):
    """Create a line y = m*x + b + N(0,sigma**2) between x=[-1.0,1.0]
    m : float
        The slope of the line.
    b : float
        The y-intercept of the line.
    sigma : float
        The standard deviation of the y direction normal distribution noise.
    size : int
        The number of points to create for the line.
    x : array of floats
        The array of x values for the line with `size` points.
    y : array of floats
        The array of y values for the lines with `size` points.
    x = np.linspace(-1.0, 1.0, size)
    if sigma > 0:
        N = np.random.normal(0, sigma, size)
        N = 0
    y = m*x + b + N
    return(x, y)

In [11]:
m = 0.0; b = 1.0; sigma=0.0; size=3
x, y = random_line(m, b, sigma, size)
assert len(x)==len(y)==size
assert list(x)==[-1.0,0.0,1.0]
assert list(y)==[1.0,1.0,1.0]
sigma = 1.0
m = 0.0; b = 0.0
size = 500
x, y = random_line(m, b, sigma, size)
assert np.allclose(np.mean(y-m*x-b), 0.0, rtol=0.1, atol=0.1)
assert np.allclose(np.std(y-m*x-b), sigma, rtol=0.1, atol=0.1)

Write a function named plot_random_line that takes the same arguments as random_line and creates a random line using random_line and then plots the x and y points using Matplotlib's scatter function:

  • Make the marker color settable through a color keyword argument with a default of red.
  • Display the range $x=[-1.1,1.1]$ and $y=[-10.0,10.0]$.
  • Customize your plot to make it effective and beautiful.

In [12]:
def ticks_out(ax):
    """Move the ticks to the outside of the box."""
    ax.get_xaxis().set_tick_params(direction='out', width=1, which='both')
    ax.get_yaxis().set_tick_params(direction='out', width=1, which='both')

In [37]:
def plot_random_line(m, b, sigma, size=10, color='red'):
    """Plot a random line with slope m, intercept b and size points."""
    plt.ylim(-10.0, 10.0)
    plt.title("y  = mx + b + N (0, $\sigma$ ** 2)", fontsize=16)
    ax = plt.gca()
    x, y = random_line(m,b,sigma,size=10)

In [38]:
plot_random_line(5.0, -1.0, 2.0, 50)

In [ ]:
assert True # use this cell to grade the plot_random_line function

Use interact to explore the plot_random_line function using:

  • m: a float valued slider from -10.0 to 10.0 with steps of 0.1.
  • b: a float valued slider from -5.0 to 5.0 with steps of 0.1.
  • sigma: a float valued slider from 0.0 to 5.0 with steps of 0.01.
  • size: an int valued slider from 10 to 100 with steps of 10.
  • color: a dropdown with options for red, green and blue.

In [41]:
interact(plot_random_line, m=(-10.0,10.0,0.1), b=(-5.0,5.0,0.1), sigma=(0.0,5.0,0.01), size=(10,100,10), color={"red":'red', "green": 'green', "blue":'blue'});

In [40]:
#### assert True # use this cell to grade the plot_random_line interact

In [ ]: