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:

MDS

Gallery examples: Comparison of Manifold Learning methods Manifold Learning methods on a severed sphere Manifold learning on handwritten digits: Locally Linear Embedding, Isomap… Multi-dimensional ...

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 3.10.3 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 3.10.3 documentation

matplotlib.org

This ... is real

 

[soundofsilence]

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

MDS

Gallery examples: Comparison of Manifold Learning methods Manifold Learning methods on a severed sphere Manifold learning on handwritten digits: Locally Linear Embedding, Isomap… Multi-dimensional ...

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 3.10.3 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 3.10.3 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

 

[Stargazer]

Any update coming to the pots in the near future @dogmeat?

 

[dogmeat]

I've relied on nscstats.com, so I don't have the recent data, unless I gather it myself.

This is the state for current nations, without the ones debuting post-226. However, all three remaining nations (Bala Kunmenai, UK of Destrion, Vermilion) look like a good fit in the middle pot, and it would mean we have 5 equal pots of 12 nations.

(after posting this, I'm not entirely sure if @Alevender 's previous nation was called Uferbruht, or if I mistook it for something else)

 

[Thorn]

(after posting this, I'm not entirely sure if @Alevender 's previous nation was called Uferbruht, or if I mistook it for something else)

Yes, it was.

 

[Paper7]

I'm the center of the potiverse! Yeah! Because i have no taste-friends tbh. ^^

 

[nofuxCZ]

I've relied on nscstats.com, so I don't have the recent data, unless I gather it myself.

Sorry about that. I just got back from my travels in Asia so I will update it during the weekend.

 

[Marcos C]

Since no numbers have been assigned to the five pots, I'm simply naming the pots after their respective colors for this edition (e.g., the Orange Pot). I have just added the participants' pots to the event spreadsheet that will be published after the final results show.

 

[nofuxCZ]

I've relied on nscstats.com, so I don't have the recent data, unless I gather it myself.

Sorry about that. I just got back from my travels in Asia so I will update it during the weekend.

Sorry for being late again, but I finally processed all the missing voting data for 227-238, so nscstats.com is up to date now.
Here is the export of the average voting similarities for the past 10 editions https://nscstats.com/NSCVoting/NSC_Voting_Similarity_229_238.xlsx