def getMCN(trade, tradesmen, population):
fig, ax = plt.subplots()
neighbourhood = [x[trade] for n, x in tradesmen.items()]
totalTradesmen = sum(neighbourhood)
if(totalTradesmen > 20):
perCapita = [n / p for n, p in zip(neighbourhood, population)]
plt.title(Shared.getTradeName(trade))
bimodal = analyze(fig, ax, trade, perCapita)
return bimodal
return -1
px = pixels[x, y]
for i, n in enumerate(Shared.getNeighbourhoods().keys()):
g = int(n / 10)
b = n % 10
if px[0] == 0 and px[1] == g and px[2] == b:
pixels[x, y] = (int(values[i] * 255), 0, 0, 255)
fm.save("results/trademap_" + str(trade) + ".png")
return fm
def getMCN(trade, tradesmen, population):
fig, ax = plt.subplots()
neighbourhood = [x[trade] for n, x in tradesmen.items()]
totalTradesmen = sum(neighbourhood)
if(totalTradesmen > 20):
perCapita = [n / p for n, p in zip(neighbourhood, population)]
plt.title(Shared.getTradeName(trade))
bimodal = analyze(fig, ax, trade, perCapita)
return bimodal
return -1
tradesmen, population, tradeWealth = Shared.getCatastoData()
centralized = []
decentralized = []
for trade in range(0,100):
bimodal = getMCN(trade, tradesmen, population)
if bimodal:
centralized.append(Shared.getTradeName(trade))
else:
decentralized.append(Shared.getTradeName(trade))
print(centralized)
print(decentralized)
ap = sklearn.cluster.AffinityPropagation(damping = 0.9 , affinity='precomputed')
#ap = sklearn.cluster.KMeans()
wmin = scipy.mean(c)
#ap = sklearn.cluster.MeanShift(bandwidth=100, cluster_all=False)
#ap.fit(c)
labels = ap.fit_predict(c)
print(labels)
tl = []
Gl = []
for label in range(min(labels), max(labels) +1):
tl.append([])
Gl.append(nx.Graph())
for i in range(len(labels)):
if(labels[i] == label):
trade = validTrades[i]
tl[label].append(Shared.getTradeName(trade))
Gl[label].add_node(Shared.getTradeName(trade))
weights = []
for i in range(len(labels)):
if(labels[i] == label):
for j in range(len(labels)):
if(labels[j] == label):
for x in range(1,4):
highTrade = np.argmax(c[i] == sorted(c[i], reverse = True)[x])
w = c[i][highTrade]
Gl[label - min(labels)].add_edge(Shared.getTradeName(i),Shared.getTradeName(highTrade),weight=w)
# if(highTrade > i):
# weights.append(w)
for node in Gl[label-min(labels)].nodes():