Advanced NumPy

A 3h00 course on advanced numpy techniques
Nicolas P. Rougier, G-Node summer school, Camerino, Italy, 2018

NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.

– Wikipedia

Quicklinks: Numpy website – Numpy GitHub – Numpy documentation – ASPP archives – 100 Numpy Exercises – From Python to Numpy

❶ – Introduction

NumPy is all about vectorization. If you are familiar with Python, this is the main difficulty you'll face because you'll need to change your way of thinking and your new friends (among others) are named "vectors", "arrays", "views" or "ufuncs". Let's take a very simple example: random walk.

One obvious way to write a random walk in Python is:

def random_walk_slow(n):
    position = 0
    walk = [position]
    for i in range(n):
        position += 2*random.randint(0, 1)-1
        walk.append(position)
    return walk
walk = random_walk_slow(1000)

It works, but it is slow. We can do better using the itertools Python module that offers a set of functions for creating iterators for efficient looping. If we observe that a random walk is an accumulation of steps, we can rewrite the function by first generating all the steps and accumulate them without any loop:

def random_walk_faster(n=1000):
    from itertools import accumulate
    # Only available from Python 3.6
    steps = random.choices([-1,+1], k=n)
    return [0]+list(accumulate(steps))
walk = random_walk_faster(1000)

It is better but still, it is slow. A more efficient implementation, taking full advantage of NumPy, can be written as:

def random_walk_fastest(n=1000):
    steps = np.random.choice([-1,+1], n)
    return np.cumsum(steps)
walk = random_walk_fastest(1000)

Now, it is amazingly fast !

>>> timeit("random_walk_slow(1000)", globals())
Timing 'random_walk_slow(1000)'
1.58 ms ± 0.0228 ms per loop (mean ± std. dev. of 7 runs, 1000 loops each)

>>> timeit("random_walk_faster(1000)", globals())
Timing 'random_walk_faster(1000)'
281 us ± 3.15 us per loop (mean ± std. dev. of 7 runs, 10000 loops each)

>>> timeit("random_walk_fastest(1000)", globals())
Timing 'random_walk_fastest(1000)'
27.6 us ± 3.45 us per loop (mean ± std. dev. of 7 runs, 1000 loops each)

Warning: You may have noticed (or not) that the random_walk_fast works but is not reproducible at all, which is pretty annoying in Science. If you want to know why, you can have a look at the article Re-run, Repeat, Reproduce, Reuse, Replicate: Transforming Code into Scientific Contributions (that I wrote with Fabien Benureau).

Last point, before heading to the course, I would like to warn you about a potential problem you may encounter once you'll have become familiar enough with NumPy. It is a very powerful library and you can make wonders with it but, most of the time, this comes at the price of readability. If you don't comment your code at the time of writing, you won't be able to tell what a function is doing after a few weeks (or possibly days). For example, can you tell what the two functions below are doing?

def function_1(seq, sub):
    return [i for i in range(len(seq) - len(sub)) if seq[i:i+len(sub)] == sub]

def function_2(seq, sub):
    target = np.dot(sub, sub)
    candidates = np.where(np.correlate(seq, sub, mode='valid') == target)[0]
    check = candidates[:, np.newaxis] + np.arange(len(sub))
    mask = np.all((np.take(seq, check) == sub), axis=-1)
    return candidates[mask]

As you may have guessed, the second function is the vectorized-optimized-faster-NumPy version of the first function and it runs 10x faster than the pure Python version. But it is hardly readable.

❷ – Warmup

You're supposed to be already familiar with NumPy. If not, you should read the NumPy chapter from the SciPy Lecture Notes. Before heading to the more advanced stuff, let's do some warmup exercises (that should pose no problem). If you choke on the first exercise, you should try to have a look at the Anatomy of an array and check also the Quick references.

Useful tools

Before heading to the exercises, We'll write a sysinfo and info function that will help us debug our code.

The sysinfo function displays some information related to you scientific environment:

>>> import tools
>>> tools.sysinfo()
Date:       08/25/18
Python:     3.7.0
Numpy:      1.14.5
Scipy:      1.1.0
Matplotlib: 2.2.2

While the info function displays a lot of information for a specific array:

>>> import tools
>>> Z = np.arange(9).reshape(3,3)
>>> tools.info(Z)
------------------------------
Interface (item)
  shape:       (3,3)
  dtype:       int64
  length:      3
  size:        9
  endianess:   native (little)
  order:       ☑ C  ☐ Fortran

Memory (byte)
  item size:   8
  array size:  72
  strides:     (24, 8)

Properties
  own data:    ☑ Yes  ☐ No
  writeable:   ☑ Yes  ☐ No
  contiguous:  ☑ Yes  ☐ No
  aligned:     ☑ Yes  ☐ No
------------------------------

Try to code these two functions. You can then compare your implementation with mine.
NOTE: We don't care so much about the formatting, do not lose time trying to copy it exactly.

The tools.py script comes with two other functions that might be useful. The timeit function allows to precisely time some code (e.g. to measure which one is the fastest). It is pretty similar to the %timeit magic function from IPython:

>>> import tools
>>> tools.timeit("Z=np.random.uniform(0,1,1000000)", globals())
>>> Measuring time for 'Z=np.random.uniform(0,1,1000000)'
11.4 ms ± 0.198 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)

And the imshow function is able to display a one-dimensional or two-dimensional array in the console. It won't replace matplotlib but it can comes handy for some (small) arrays (you'll need a 256 colors terminal):

Basic manipulation

Let's start with some basic operations:

• Create a vector with values ranging from 10 to 49
• Create a null vector of size 100 but the fifth value which is 1
• Reverse a vector (first element becomes last)
• Create a 3x3 matrix with values ranging from 0 to 8
• Create a 3x3 identity matrix
• Create a 2d array with 1 on the border and 0 inside
• Given a 1D array, negate all elements which are between 3 and 8, in place

For a more complete list, you can have a look at the 100 Numpy Exercises.

Solution (click to expand)

Sources: basic-manipulation.py

import numpy as np

# Create a vector with values ranging from 10 to 49
Z = np.arange(10,50)

# Create a null vector of size 100 but the fifth value which is 1
Z = np.zeros(100)
Z[4] = 1

# Reverse a vector (first element becomes last)
Z = np.arange(50)[::-1]

# Create a 3x3 matrix with values ranging from 0 to 8
Z = np.arange(9).reshape(3,3)

# Create a 3x3 identity matrix
Z = np.eye(3)

# Create a 2d array with 1 on the border and 0 inside
Z = np.ones((10,10))
Z[1:-1,1:-1] = 0

# Given a 1D array, negate all elements which are between 3 and 8, in place
Z = np.arange(11)
Z[(3 < Z) & (Z <= 8)] *= -1

NaN arithmetics

Just a reminder on NaN arithmetics:

What is the result of the following expression?
→ Hints: What Every Computer Scientist Should Know About Floating-Point Arithmetic, D. Goldberg, 1991

print(0 * np.nan)
print(np.nan == np.nan)
print(np.inf > np.nan)
print(np.nan - np.nan)
print(0.3 == 3 * 0.1)

Solution (click to expand)

Sources nan-arithmetics.py

import numpy as np

# Result is NaN
print(0 * np.nan)

# Result is False
print(np.nan == np.nan)

# Result is False
print(np.inf > np.nan)

# Result is NaN
print(np.nan - np.nan)

# Result is False !!!
print(0.3 == 3 * 0.1)
print("0.1 really is {:0.56f}".format(0.1))

Computing strides

Consider an array Z, how to compute Z strides (manually)?
→ Hints: itemsize – shape – ndim

import numpy as np
Z = np.arange(24).reshape(2,3,4)
print(Z.strides)

Solution (click to expand)

Sources strides.py

import numpy as np

def strides(Z):
    strides = [Z.itemsize]
    
    # Fotran ordered array
    if np.isfortran(Z):
        for i in range(0, Z.ndim-1):
            strides.append(strides[-1] * Z.shape[i])
        return tuple(strides)
    # C ordered array
    else:
        for i in range(Z.ndim-1, 0, -1):
            strides.append(strides[-1] * Z.shape[i])
        return tuple(strides[::-1])

# This work
Z = np.arange(24).reshape((2,3,4), order="C")
print(Z.strides, " – ", strides(Z))

Z = np.arange(24).reshape((2,3,4), order="F")
print(Z.strides, " – ", strides(Z))

# This does not work
Z = Z[::2]
print(Z.strides, " – ", strides(Z))

Repeat and repeat

Can you tell the difference?
→ Hints: tile – as_strided

import numpy as np
from numpy.lib.stride_tricks import as_strided

Z = np.random.randint(0,10,5)
Z1 = np.tile(Z, (3,1))
Z2 = as_strided(Z, shape=(3,)+Z.shape, strides=(0,)+Z.strides)

Solution (click to expand)

Sources repeat.py

import numpy as np
from numpy.lib.stride_tricks import as_strided

Z = np.zeros(5)
Z1 = np.tile(Z,(3,1))
Z2 = as_strided(Z, shape=(3,)+Z.shape, strides=(0,)+Z.strides)

# Real repeat: three times the memory
Z1[0,0] = 1
print(Z1)

# Fake repeat: less memory but not totally equivalent
Z2[0,0] = 1
print(Z2)

Reordering things

Let's consider the following list:

L = [  0,   0,   0,   0,   0,   0,   3, 233,
       0,   0,   0,   0,   0,   0,   3, 237,
       0,   0,   0,   0,   0,   0,   3, 235,
       0,   0,   0,   0,   0,   0,   3, 239,
       0,   0,   0,   0,   0,   0,   3, 234,
       0,   0,   0,   0,   0,   0,   3, 238,
       0,   0,   0,   0,   0,   0,   3, 236,
       0,   0,   0,   0,   0,   0,   3, 240]

This is actually the byte dump of a 2x2x2 array, fortran ordered of 64 bits integers using big endian encoding.

How would you access element at [1,0,0] with NumPy (simple)?

Solution (click to expand)

import struct
import numpy as np

# Generation of the array
# Z = range(1001, 1009)
# L = np.reshape(Z, (2,2,2), order="F").ravel().astype(">i8").view(np.ubyte)

L = [  0,   0,   0,   0,   0,   0,   3, 233,
       0,   0,   0,   0,   0,   0,   3, 237,
       0,   0,   0,   0,   0,   0,   3, 235,
       0,   0,   0,   0,   0,   0,   3, 239,
       0,   0,   0,   0,   0,   0,   3, 234,
       0,   0,   0,   0,   0,   0,   3, 238,
       0,   0,   0,   0,   0,   0,   3, 236,
       0,   0,   0,   0,   0,   0,   3, 240]

# Automatic (numpy)
Z = np.reshape(np.array(L, dtype=np.ubyte).view(dtype=">i8"), (2,2,2), order="F")
print(Z[1,0,0])

How would you access element at [1,0,0] without NumPy (harder)?
→ Hints: Use your brain!

Solution (click to expand)

Sources reorder.py

import struct
import numpy as np

# Generation of the array
# Z = range(1001, 1009)
# L = np.reshape(Z, (2,2,2), order="F").ravel().astype(">i8").view(np.ubyte)

L = [  0,   0,   0,   0,   0,   0,   3, 233,
       0,   0,   0,   0,   0,   0,   3, 237,
       0,   0,   0,   0,   0,   0,   3, 235,
       0,   0,   0,   0,   0,   0,   3, 239,
       0,   0,   0,   0,   0,   0,   3, 234,
       0,   0,   0,   0,   0,   0,   3, 238,
       0,   0,   0,   0,   0,   0,   3, 236,
       0,   0,   0,   0,   0,   0,   3, 240]

# Automatic (numpy)
Z = np.reshape(np.array(L, dtype=np.ubyte).view(dtype=">i8"), (2,2,2), order="F")
print(Z[1,0,0])

# Manual (brain)
shape = (2,2,2)
itemsize = 8
# We can probably do better
strides = itemsize, itemsize*shape[0], itemsize*shape[0]*shape[1]
index = (1,0,0)
start = sum(i*s for i,s in zip(index,strides))
end = start+itemsize
value = struct.unpack(">Q", bytes(L[start:end]))[0]
print(value)

Heat equation

The diffusion equation (a.k.a the heat equation) reads ∂u/∂t = α∂²u/∂x² where u(x,t) is the unknown function to be solved, x is a coordinate in space, and t is time. The coefficient α is the diffusion coefficient and determines how fast u changes in time. The discrete (time(n) and space (i)) version of the equation can be rewritten as u(i,n+1) = u(i,n) + F(u(i-1,n) - 2u(i,n) + u(i+1,n)).

– Finite difference methods for diffusion processes, Hans Petter Langtangen

The goal here is to compute the discrete equation over a finite domain using as_strided to produce a sliding-window view of a 1D array. This view can be then used to compute U at the next iteration. Using the the following initial conditions (using Z instead of U):

Z = np.random.uniform(0.00, 0.05, (50,100))
Z[0,5::10] = 1

Try to obtain this picture (where time goes from top to bottom):

The code to display the figure from an array Z is:

import matplotlib as plt

plt.figure(figsize=(6,3))
plt.subplot(1,1,1,frameon=False)
plt.imshow(Z, vmin=0, vmax=1)
plt.xticks([]), plt.yticks([])
plt.tight_layout()
plt.show()

Hint: You will need to write a sliding_window(Z, size=3) function that returns a strided view of Z.

Solution (click to expand)

Sources diffusion.py

import numpy as np
import matplotlib.pyplot as plt
from numpy.lib.stride_tricks import as_strided


def sliding_window(Z, size=2):
    n, s = Z.shape[0], Z.strides[0]
    return as_strided(Z, shape=(n-size+1, size), strides=(s, s))


# Initial conditions:
# Domain size is 100 and we'll iterate over 50 time steps
Z = np.zeros((50,100))
Z[0,5::10] = 1.5

# Actual iteration
F = 0.05
for i in range(1, len(Z)):
    Z[i,1:-1] = Z[i-1,1:-1] + F*(sliding_window(Z[i-1], 3)*[+1,-2,+1]).sum(axis=1)

# Display
plt.figure(figsize=(6,3))
plt.subplot(1,1,1,frameon=False)
plt.imshow(Z, vmin=0, vmax=1)
plt.xticks([]), plt.yticks([])
plt.tight_layout()
plt.savefig("diffusion.png")
plt.show()

Rule 30

With only a slight modification of the previous exercise, we can compute a one-dimensional cellular automata and more specifically the Rule 30 that exhibits intriguing patterns as shown below:

To start with, here is how to convert the rule in a useful form:

rule = 30 
R = np.array([int(v) for v in '{0:08b}'.format(rule)])[::-1]

and we consider this initial state:

Z = np.zeros((250,501), dtype=int)
Z[0,250] = 1

Try to obtain the same figure. Display code is:

plt.figure(figsize=(6,3))
plt.subplot(1,1,1,frameon=False)
plt.imshow(Z, vmin=0, vmax=1, cmap=plt.cm.gray_r)
plt.xticks([]), plt.yticks([])
plt.tight_layout()
plt.savefig("automata.png")
plt.show()

Solution (click to expand)

Sources automata.py

import numpy as np
import matplotlib.pyplot as plt
from numpy.lib.stride_tricks import as_strided

def sliding_window(Z, size=2):
    n, s = Z.shape[0], Z.strides[0]
    return as_strided(Z, shape=(n-size+1, size), strides=(s, s))

# Rule 30  (see https://en.wikipedia.org/wiki/Rule_30)
# 0x000: 0, 0x001: 1, 0x010: 1, 0x011: 1
# 0x100: 1, 0x101: 0, 0x110: 0, 0x111: 0
rule = 30 
R = np.array([int(v) for v in '{0:08b}'.format(rule)])[::-1]

# Initial state
Z = np.zeros((250,501), dtype=int)
Z[0,250] = 1

# Computing some iterations
for i in range(1, len(Z)):
    N = sliding_window(Z[i-1],3) * [1,2,4]
    Z[i,1:-1] = R[N.sum(axis=1)]

# Display
plt.figure(figsize=(6,3))
plt.subplot(1,1,1,frameon=False)
plt.imshow(Z, vmin=0, vmax=1, cmap=plt.cm.gray_r)
plt.xticks([]), plt.yticks([])
plt.tight_layout()
plt.savefig("automata.png")
plt.show()

Input / Output

→ Exercise written by Stefan van der Walt.

Place the following data in a text file, data.txt:

% rank         lemma (10 letters max)      frequency       dispersion
21             they                        1865844         0.96
42             her                         969591          0.91
49             as                          829018          0.95
7              to                          6332195         0.98
63             take                        670745          0.97
14             you                         3085642         0.92
35             go                          1151045         0.93
56             think                       772787          0.91
28             not                         1638883         0.98

Now, design a suitable structured data type, then load the data from the text file using np.loadtxt (look at the documenration to see how to handle the '%' comment character).

Here's a skeleton to start with:

import numpy as np

# Construct the data-type
# For example:
# dtype = np.dtype([('x', np.float), ('y', np.int), ('z', np.uint8)])

dt = np.dtype(...)  # Modify this line to give the correct answer
data = np.loadtxt(...)  # Load data with loadtxt

Examine the data you got:

Extract words only
Extract the 3rd row
Print all words with rank < 30

Sort the data according to frequency (see np.sort).

Save the result to a compressed numpy data file (e.g. "sorted.npz") using np.savez and load it back with out = np.load("sorted.npz"). Do you get back what you put in? Why?

Solution (click to expand)

Source: input-output.py

import numpy as np

# Create our own dtype
dtype = np.dtype([('rank',       'i8'),
                  ('lemma',      'S8'),
                  ('frequency',  'i8'),
                  ('dispersion', 'f8')])

# Load file using our own dtype
data = np.loadtxt('data.txt', comments='%', dtype=dtype)

# Extract words only
print(data["lemma"])

# Extract the 3rd row
print(data[2])

# Print all words with rank < 30
print(data[data["rank"] < 30])

# Sort the data according to frequency.
sorted = np.sort(data, order="frequency")
print(sorted)

# Save unsorted and sorted array
np.savez("sorted.npz", data=data, sorted=sorted)

# Load saved array
out = np.load("sorted.npz")
print(out["sorted"])

❸ – Advanced exercises

Geometry

We consider a collection of 2d squares that are each defined by four points, a scaling factor, a translation and a rotation angle. We want to obtain the following figure:

made of 25 squares, scaled by 0.1, translated by (1,0) and with increasing rotation angles. The order of operation is scale, translate and rotate. What would be the best structure S to hold all these information at once?
→ Hints: structured arrays

Solution (click to expand)

dtype = [("points",    float, (4, 2)),
         ("scale",     float, 1),
         ("translate", float, 2),
         ("rotate",    float, 1)]
S = np.zeros(25, dtype = dtype)

We now need to initialize our array. For the four points describing a square, you can use the following points: [(-1,-1), (-1,+1), (+1,+1), (+1,-1)]

Solution (click to expand)

S["points"] = [(-1,-1), (-1,+1), (+1,+1), (+1,-1)]
S["translate"] = (1,0)
S["scale"] = 0.1
S["rotate"] = np.linspace(0, 2*np.pi, len(S), endpoint=False)

Now, we need to write a function that apply all these transformations and write the results in new array:

P = np.zeros((len(S), 4, 2))
# Your code here (to populate P)
...

You can start by writing a translate, scale and rotate function first.

Rotation reminder. Considering a point (x,y) and a rotation angle a, the rotated coordinates (x',y') are:

x' = x.cos(a) - y.sin(a) and y' = x.sin(a) + y.cos(a)

The display code is:

import matplotlib.pyplot as plt

fig = plt.figure(figsize=(6,6))
ax = plt.subplot(1,1,1, frameon=False)
for i in range(len(P)):
    X = np.r_[P[i,:,0], P[i,0,0]]
    Y = np.r_[P[i,:,1], P[i,0,1]]
    plt.plot(X, Y, color="black")
plt.xticks([]), plt.yticks([])
plt.tight_layout()
plt.show()

Solution (click to expand)

Source: geometry.py

import numpy as np
import matplotlib.pyplot as plt

dtype = [("points",    float, (4, 2)),
         ("scale",     float, 1),
         ("translate", float, 2),
         ("rotate",    float, 1)]
S = np.zeros(25, dtype = dtype)
S["points"] = [(-1,-1), (-1,+1), (+1,+1), (+1,-1)]
S["translate"] = (1,0)
S["scale"] = 0.1
S["rotate"] = np.linspace(0, 2*np.pi, len(S), endpoint=False)

P = np.zeros((len(S), 4, 2))
for i in range(len(S)):
    for j in range(4):
        x = S[i]["points"][j,0]
        y = S[i]["points"][j,1]
        tx, ty = S[i]["translate"]
        scale  = S[i]["scale"]
        theta  = S[i]["rotate"]
        x = tx + x*scale
        y = ty + y*scale
        x_ = x*np.cos(theta) - y*np.sin(theta)
        y_ = x*np.sin(theta) + y*np.cos(theta)
        P[i,j] = x_, y_

fig = plt.figure(figsize=(6,6))
ax = plt.subplot(1,1,1, frameon=False)
for i in range(len(P)):
    X = np.r_[P[i,:,0], P[i,0,0]]
    Y = np.r_[P[i,:,1], P[i,0,1]]
    plt.plot(X, Y, color="black")
plt.xticks([]), plt.yticks([])
plt.tight_layout()
plt.savefig("geometry.png")
plt.show()

The proposed solution has two loops. Can you imagine a way to do it without loop ?
→ Hints: einsum

Solution (click to expand)

Have a look at Multiple individual 2d rotation at once on stack overflow. I did not implement it, feel free to issue a PR with the solution.

Image quantization

In computer graphics, color quantization or color image quantization is quantization applied to color spaces; it is a process that reduces the number of distinct colors used in an image, usually with the intention that the new image should be as visually similar as possible to the original image.

– Wikipedia

In this exercise, we want to produce color quantization, that is, considering a random image, we would like to reduce the number of colors without altering too much the perception of the image. We thus need to find the most representative colors.

The first (naive) idea that may come to mind is to count the number of times a specific color is used and to use the most frequent colors for quantization. Unfortunately, this does not work very well as illustrated below:

The reason is that some color and slight variations might be over-represented in th eoriginal image and will thus appears among the most frequent colors. This the reason why the kitten ended mostly in green and the flower totally dissapeared.

To check by yourself, you'll write the corresponding script and check for the result:

Load an image (using imageio.imread)
Find the number of unique colors and their frequency (counts)
Pick the n=16 most frequent colors
Replace colors in the original image with the closest color (found previously)
Save the result (using imageio.imsave)

Solution (click to expand)

Sources: bad-dither.py

import imageio
import numpy as np
import scipy.spatial

# Number of final colors we want
n = 16

# Original Image
I = imageio.imread("kitten.jpg")
shape = I.shape

# Flattened image
I = I.reshape(shape[0]*shape[1], shape[2])

# Find the unique colors and their frequency (=counts)
colors, counts = np.unique(I, axis=0, return_counts=True)

# Get the n most frequent colors
sorted = np.argsort(counts)[::-1]
C = I[sorted][:n]

# Compute distance to most frequent colors
D = scipy.spatial.distance.cdist(I, C, 'sqeuclidean')

# Replace colors with closest one
Z = (C[D.argmin(axis=1)]).reshape(shape)

# Save result
imageio.imsave("kitten-dithered.jpg", Z)

We thus need a different method and this method is called k-means clustering that allow to partition data into n clusters whose centroids may serve as a prototype for the cluster.

The algorithm is quite simple. We start with n random points (centroids) and we compute for each point in our data what is the closest centroid. Those constitute cluster of points. For each cluster, we compute its centroid (mean point) and we reiterate the processus for a given number of steps. In this exercise, you'll have to write such a k-means function and to use it to quantize the image.

Solution (click to expand)

Sources: kmeans.py

# Code by Gareth Rees, posted on stack overflow
# https://codereview.stackexchange.com/questions/61598/k-mean-with-numpy

import numpy as np
import scipy.spatial

def cluster_centroids(data, clusters, k=None):
    if k is None:
        k = np.max(clusters) + 1
    result = np.empty(shape=(k,) + data.shape[1:])
    for i in range(k):
        np.mean(data[clusters == i], axis=0, out=result[i])
    return result


def kmeans(data, k=None, centroids=None, steps=20):
    if centroids is not None and k is not None:
        assert(k == len(centroids))
    elif centroids is not None:
        k = len(centroids)
    elif k is not None:
        # Forgy initialization method: choose k data points randomly.
        centroids = data[np.random.choice(np.arange(len(data)), k, False)]
    else:
        raise RuntimeError("Need a value for k or centroids.")

    for _ in range(max(steps, 1)):
        # Squared distances between each point and each centroid.
        sqdists = scipy.spatial.distance.cdist(centroids, data, 'sqeuclidean')

        # Index of the closest centroid to each data point.
        clusters = np.argmin(sqdists, axis=0)

        new_centroids = cluster_centroids(data, clusters, k)
        if np.array_equal(new_centroids, centroids):
            break

        centroids = new_centroids
    return centroids, clusters


if __name__ == '__main__':
    import imageio

    # Number of final colors we want
    n = 16

    # Original Image
    I = imageio.imread("kitten.jpg")
    shape = I.shape

    # Flattened image
    D = I.reshape(shape[0]*shape[1], shape[2])
    
    # Search for 16 centroids in D (using 20 iterations)
    centroids, clusters = kmeans(D, k=n, steps=20)

    # Create quantized image
    I = (centroids[clusters]).reshape(shape)
    I = np.round(I).astype(np.uint8)

    # Save result
    imageio.imsave("kitten-quantized.jpg", I)

Neural networks

In this exercise, we'll implement one of the most simple feed-forward neural network, a.k.a. the Perceptron. We'll use it to discrimate between two classes (points in two dimensions,see desired output):

samples = np.zeros(100, dtype=[('input',  float, 2),
                               ('output', float, 1)])
                               
P = np.random.uniform(0.05,0.95,(len(samples),2))
samples["input"] = P
stars = np.where(P[:,0]+P[:,1] < 1)
discs = np.where(P[:,0]+P[:,1] > 1)
samples["output"][stars] = +1
samples["output"][discs] = 0

Your goal is to populate the following class in order to train the network. You'll need:

a one-dimensional array to store the input
a one-dimensional array to store the output
a two-dimensional array to store the weights
a threshold function (for example lambda x: x > 0)

The propagate_forward method is supposed to compute the output of the network while the propagate_backward is supposed to modify the weights according to the actual error.

class Perceptron:
    def __init__(self, n, m):
        "Initialization of the perceptron with given sizes"
        ...

    def reset(self):
        "Reset weights"
        ...

    def propagate_forward(self, data):
        "Propagate data from input layer to output layer"
        ...
        
    def propagate_backward(self, target, lrate=0.1):
        "Back propagate error related to target using lrate"
        ...

Solution (click to expand)

Sources: perceptron.py

class Perceptron:
    ''' Perceptron class. '''

    def __init__(self, n, m):
        "Initialization of the perceptron with given sizes"

        self.input  = np.ones(n+1)
        self.output = np.ones(m)
        self.weights= np.zeros((m,n+1))
        self.reset()

    def reset(self):
        "Reset weights"

        self.weights[...] = np.random.uniform(-.5, .5, self.weights.shape)

    def propagate_forward(self, data):
        "Propagate data from input layer to output layer"

        # Set input layer (but not bias)
        self.input[1:]  = data
        self.output[...] = f(np.dot(self.weights,self.input))

        # Return output
        return self.output

    def propagate_backward(self, target, lrate=0.1):
        "Back propagate error related to target using lrate"

        error = np.atleast_2d(target-self.output)
        input = np.atleast_2d(self.input)
        self.weights += lrate*np.dot(error.T,input)

        # Return error
        return (error**2).sum()

To train the network for 1000 iterations, we can do:

lrate = 0.1
for i in range(1000):
    lrate *= 0.999
    n = np.random.randint(samples.size)
    network.propagate_forward( samples['input'][n] )
    error = network.propagate_backward( samples['output'][n], lrate )

For other type of neural networks, you can have a look at https://github.com/rougier/neural-networks/.

❹ – References

Book & tutorials

This is a curated list of resources among the plethora of books & tutorials that exist online. Make no mistake, it is strongly biased.

From Python to Numpy, Nicolas P.Rougier, 2017
100 Numpy Exercises, Nicolas P. Rougier, 2017
SciPy Lecture Notes, Gaël Varoquaux, Emmanuelle Gouillart, Olav Vahtras et al., 2016
Elegant SciPy: The Art of Scientific Python, Juan Nunez-Iglesias, Stéfan van der Walt, Harriet Dashnow, 2016
Numpy Medkit, Stéfan van der Walt, 2008

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Repository files navigation

Advanced NumPy

Table of Contents

❶ – Introduction

❷ – Warmup

Useful tools

Basic manipulation

NaN arithmetics

Computing strides

Repeat and repeat

Reordering things

Heat equation

Rule 30

Input / Output

❸ – Advanced exercises

Geometry

Image quantization

Neural networks

❹ – References

Book & tutorials

Archives

About

Releases

Packages

Languages

Name		Name	Last commit message	Last commit date
Latest commit History 31 Commits
LICENSE.md		LICENSE.md
README.md		README.md
automata.png		automata.png
automata.py		automata.py
bad-dither.py		bad-dither.py
basic-manipulation.py		basic-manipulation.py
data.txt		data.txt
diffusion.png		diffusion.png
diffusion.py		diffusion.py
dithered.png		dithered.png
geometry.png		geometry.png
geometry.py		geometry.py
imshow.png		imshow.png
imshow.py		imshow.py
input-output.py		input-output.py
kitten-dithered.jpg		kitten-dithered.jpg
kitten-quantized.jpg		kitten-quantized.jpg
kitten.jpg		kitten.jpg
kmeans.py		kmeans.py
moving-average.py		moving-average.py
nan-arithmetics.py		nan-arithmetics.py
original.png		original.png
perceptron.mp4		perceptron.mp4
perceptron.py		perceptron.py
random-walk.py		random-walk.py
reorder.py		reorder.py
repeat.py		repeat.py
strides.py		strides.py
tools.py		tools.py

License

mczerwinski/ASPP-2018-numpy

Folders and files

Latest commit

History

Repository files navigation

Advanced NumPy

Table of Contents

❶ – Introduction

❷ – Warmup

Useful tools

Basic manipulation

NaN arithmetics

Computing strides

Repeat and repeat

Reordering things

Heat equation

Rule 30

Input / Output

❸ – Advanced exercises

Geometry

Image quantization

Neural networks

❹ – References

Book & tutorials

Archives

About

Resources

License

Stars

Watchers

Forks

Releases

Packages 0

Languages

Packages