import tensorflow.keras as ks
import networkx as nx
import numpy as np

from random import random
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

def randomGraphs(n,m,p,q,regular=True):
    X = []
    y = []
    for i in range(n):
        s = random()
        r = p + (q-p)*random()
        cls = 1
        if s<0.5:
            res = nx.random_graphs.fast_gnp_random_graph(m,r)
            cls = 0
        elif regular:
            res = nx.random_graphs.random_regular_graph(int(m*r),m)
        else:
            ell = int(s*m)
            res = nx.bipartite.random_graph(ell,m-ell,r)    
        X.append(np.array(nx.adjacency_matrix(res).toarray().flatten()))
        y.append(cls)
    return (np.array(X),np.array(y))

def Experiment(N,m,p,q,regular=True,epochs=10):
    X,y = randomGraphs(N,m,p,q,regular)
    x_train, x_test, y_train, y_test = train_test_split(X,y)

    model = ks.Sequential()
    model.add(ks.layers.Dense(m*m,activation='relu'))
    model.add(ks.layers.Dropout(0.5))
    model.add(ks.layers.Dense(m,activation='relu'))
    model.add(ks.layers.Dropout(0.25))
    model.add(ks.layers.Dense(1,activation='sigmoid'))
    model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])
    model.fit(x_train, y_train, epochs=epochs, batch_size=int(N/epochs))

    y_pred = model.predict_classes(x_test)
    return accuracy_score(y_pred,y_test)

print(Experiment(2000,50,0.2,0.3,True,25))

Epoch 1/25
19/19 [==============================] - 1s 52ms/step - loss: 0.7299 - accuracy: 0.5740
...
Epoch 25/25
19/19 [==============================] - 1s 49ms/step - loss: 0.0354 - accuracy: 0.9900

0.908

print(Experiment(1200,50,0.2,0.3,False,10))

Epoch 1/10
8/8 [==============================] - 1s 50ms/step - loss: 0.6087 - accuracy: 0.7289
...
Epoch 10/10
8/8 [==============================] - 0s 51ms/step - loss: 0.0046 - accuracy: 1.0000

1.0

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[2020-12-12] Havel–Hakimi Algorithm in Common-Lisp

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[2018-02-28] Irreducible Dyck Words

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[2018-02-08] Linus Sequence

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[2018-01-23] Collatz sequence (yet again)

[2018-01-15] Hofstadter's Q sequence

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[2016-08-16] Puzzles and Group Theory

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[2016-04-18] A Migration Analysis

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[2015-10-07] Computational Literary Analysis

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[2014-09-29] A Problem on Substitution Ciphers and Group Theory

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[2013-02-03] Distribution of Collatz Lengths

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[2013-01-12] Path ideals

[2013-01-10] McCarthy91 Terminates

[2013-01-09] Finding all paths in a directed graph

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[2012-10-10] An implementation of the k-means clustering algorithm in lisp

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[2012-10-03] Source code entropy

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