Pots (NSC 227 update)

[dogmeat]

Pot 1 Adamsburg Balearica Island Denmark of Spears Fervorosia Moisantia Perryfornia Roseland Svobodnia Tamausa & Deltannor Tanoiro Xhuxhmaxhuxh Yeto-Ya-Yeto	Pot 2 Biflovatia Cherniya Cydoni-Gibberia Fierraria Halito Illumia Oosingimaed Waiting Iist	Pot 3 Elvaci MatiMati Reym-L-Dneurb Serenes Sodermalm Tir an Abhainn Ugaly Yaponesia
Pot 4 Comino Dalisska Effiland Fervorosia Kamande New Acadia Papendink Szimbaya Kingdom Tcher-Racoi Xochimilia	Pot 5 Begonia Calypso Doire GD Strenci Insomnea Konthena Necluda New Bander State Orangualia Rahasia-Diati	Pot 6 Aimulli Akatsuki Belvist Endore Kordavian Islands Marcobia Redwood Republic Rumia Sakuralia Trollheimr Vylkuzeme Zombira

 

[soundofsilence]

@dogmeat I just came across a nice way of doing this today, if you're interested:

sklearn.manifold.MDS

Examples using sklearn.manifold.MDS: Comparison of Manifold Learning methods Manifold Learning methods on a severed sphere Manifold learning on handwritten digits: Locally Linear Embedding, Isomap…...

scikit-learn.org

You can use the MDS class in scikit-learn to generate a 2-dimensional representation of the similarity data in which the distances respect the distances in the original 60-dimensional space. Should have a similar result to your current push-pull method.

Then you can feed the result into a Matplotlib scatter plot:

matplotlib.pyplot.scatter — Matplotlib 3.8.4 documentation

matplotlib.org

There's a parameter that allows you to colour the points in the scatter plot according to an array of integers; you can use the output of the k-means clustering here and it will give each pot a different colour.

You can also add nation labels to the points in the plot using Matplotlib's annotate() function:

matplotlib.pyplot.annotate — Matplotlib 3.8.4 documentation

matplotlib.org

This ... is real

 

[soundofsilence]

@dogmeat I just came across a nice way of doing this today, if you're interested:

sklearn.manifold.MDS

Examples using sklearn.manifold.MDS: Comparison of Manifold Learning methods Manifold Learning methods on a severed sphere Manifold learning on handwritten digits: Locally Linear Embedding, Isomap…...

scikit-learn.org

You can use the MDS class in scikit-learn to generate a 2-dimensional representation of the similarity data in which the distances respect the distances in the original 60-dimensional space. Should have a similar result to your current push-pull method.

Then you can feed the result into a Matplotlib scatter plot:

matplotlib.pyplot.scatter — Matplotlib 3.8.4 documentation

matplotlib.org

There's a parameter that allows you to colour the points in the scatter plot according to an array of integers; you can use the output of the k-means clustering here and it will give each pot a different colour.

You can also add nation labels to the points in the plot using Matplotlib's annotate() function:

matplotlib.pyplot.annotate — Matplotlib 3.8.4 documentation

matplotlib.org

Tomorrow, I'll teach the code

 

[soundofsilence]

import matplotlib.pyplot as plt
import numpy as np

# Fixing random state for reproducibility
np.random.seed(3450)

# Compute areas and colors
N = 60
r = 10 * np.random.rand(N)
theta = 6 * np.pi * np.random.rand(N)
area = 59 * r**1.4
colors = theta

fig = plt.figure()
ax = fig.add_subplot(projection='polar')
c = ax.scatter(theta, r, c=colors, s=area, cmap='hsv', alpha=0.833)

 

[soundofsilence]

import matplotlib.pyplot as plt
import numpy as np

# Fixing random state for reproducibility
np.random.seed(3450)


N = 60
x = np.random.rand(N)
y = np.random.rand(N)
colors = np.random.rand(N)
area = (48 * np.random.rand(N))**1.333 # 0 to 15 point radii

plt.scatter(x, y, s=area, c=colors, alpha=0.833)
plt.show()

 

[soundofsilence]

import matplotlib.pyplot as plt
import numpy as np

# Fixing random state for reproducibility
np.random.seed(3450)


N = 60
x = np.random.rand(N)
y = np.random.rand(N)
colors = np.random.rand(N)
area = (48 * np.random.rand(N))**1.333 # 0 to 15 point radii

plt.scatter(x, y, s=area, c=colors, alpha=0.833)
plt.show()

For the original xy dots