endolith · December 27, 2020 18:57 · Jun 10, 2017 · Jun 10, 2017 · Dec 23, 2016
diff --git a/readme.md b/readme.md
@@ -1,8 +1,10 @@
 See also:
 
+- 1D: 
+  - http://zesty.ca/voting/voteline/
 - 2D:
   - http://bolson.org/voting/sim_one_seat/www/spacegraph.html
   - http://zesty.ca/voting/sim/
   - http://rangevoting.org/IEVS/Pictures.html
-- 1D: 
-  - http://zesty.ca/voting/voteline/
+- ND:  
+  - https://github.com/electology/vse-sim
diff --git a/readme.md b/readme.md
@@ -1,8 +1,8 @@
 See also:
 
 - 2D:
- - http://bolson.org/voting/sim_one_seat/www/spacegraph.html
- - http://zesty.ca/voting/sim/
- - http://rangevoting.org/IEVS/Pictures.html
+  - http://bolson.org/voting/sim_one_seat/www/spacegraph.html
+  - http://zesty.ca/voting/sim/
+  - http://rangevoting.org/IEVS/Pictures.html
 - 1D: 
   - http://zesty.ca/voting/voteline/
diff --git a/election_simulator.py b/election_simulator.py
@@ -0,0 +1,125 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Dec 17 11:33:35 2016
+"""
+from __future__ import division, print_function
+from numpy.random import multivariate_normal
+import matplotlib.pyplot as plt
+from scipy.spatial.distance import cdist
+import numpy as np
+
+# Polarization between the two main factions
+pol = np.random.randn(1)[0]
+
+voters = np.concatenate((multivariate_normal(mean=(-pol, -pol),
+                                             cov=((1, 0), (0, 1)), size=500),
+                         multivariate_normal(mean=(+pol, +pol),
+                                             cov=((1, 0), (0, 1)), size=500)
+                         ))
+
+# Candidates distributed similarly to voters
+candidates = multivariate_normal(mean=(0, 0),
+                                 cov=np.cov(voters, rowvar=False),
+                                 size=9
+                                 )
+
+plt.figure()
+plt.scatter(voters[:, 0], voters[:, 1], color='b', marker='.', alpha=0.2,
+            label='voters')
+plt.scatter(candidates[:, 0], candidates[:, 1], color='r', marker='o',
+            alpha=0.7, label='candidates')
+
+centroid = np.mean(voters, axis=0)
+plt.plot(centroid[0], centroid[1], color='k', marker='+', markersize=10,
+         ls='none', label='centroid')
+
+dists = cdist(voters, candidates)
+
+
+def plurality(dists):
+    """
+    Honest plurality voting
+    """
+    # Voters choose the nearest candidate
+    ballots = np.argmin(dists, axis=1)
+
+    # Winner is the candidate with the most votes
+    winner = np.argmax(np.bincount(ballots))
+    return ballots, winner
+
+
+def approval_nearest(dists, n=3):
+    """
+    Honest approval voting of the nearest `n` candidates
+    """
+    # Voters choose the n nearest candidates
+    ballots = np.argsort(dists, axis=1)[:, :n]
+
+    # Winner is the candidate with the most votes
+    winner = np.argmax(np.bincount(ballots.flat))
+    return ballots, winner
+
+
+def approval_half(dists):
+    """
+    Honest approval voting of the nearest 50% of candidates
+    """
+    n_cands = dists.shape[1]
+    return approval_nearest(dists, n=int(0.5 * n_cands))
+
+
+def score_normed(dists, levels=None):
+    """
+    Honest score voting, normalized to min and max for worst and best,
+    optionally quantized to number of `levels`. All candidates are scored, so
+    total vs average is irrelevant.
+    """
+    # Normalize from 0 to 1 for nearest to farthest
+    normed = dists - np.amin(dists, axis=1)[:, np.newaxis]
+    normed /= np.amax(normed, axis=1)[:, np.newaxis]
+
+    # Farthest candidates get the lowest scores
+    normed = 1 - normed
+
+    # Optionally quantize to discrete scale
+    if levels is not None:
+        normed = np.around(normed * levels)
+
+    # Total scores for each candidate
+    scores = np.sum(normed, axis=0)
+
+    # Winner is the candidate with the highest total/average score
+    winner = np.argmax(scores)
+    return ballots, winner
+
+
+def next_color():
+    ax = plt.gca()
+    while True:
+        color = next(ax._get_lines.prop_cycler)['color']
+        if color not in {'r', }:  # , 'b'}:
+            return color
+
+
+def next_size():
+    s = 6
+    while True:
+        yield s
+        s += 2
+
+sizes = next_size()
+
+
+for func in plurality, approval_nearest, approval_half, score_normed:
+    name = func.__name__.replace('_', ' ')
+    ballots, winner = func(dists)
+    winner_loc = candidates[winner]
+    plt.plot(winner_loc[0], winner_loc[1], marker='o', markersize=sizes.next(),
+             ls='none', markeredgewidth=1, markeredgecolor=next_color(),
+             color='none',  # don't increment color cycle for invisible face
+             markerfacecolor='none', label=name)
+
+plt.legend(loc='lower right', numpoints=1, fontsize='small')
+plt.axis('square')
+plt.grid(True)
+plt.tight_layout()
diff --git a/readme.md b/readme.md
@@ -0,0 +1,8 @@
+See also:
+
+- 2D:
+ - http://bolson.org/voting/sim_one_seat/www/spacegraph.html
+ - http://zesty.ca/voting/sim/
+ - http://rangevoting.org/IEVS/Pictures.html
+- 1D: 
+  - http://zesty.ca/voting/voteline/
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