evaluate_gaussian_max_MK_L2h.py

N = 120
K = [2] #,3,4,5] #,2,3,4,5] #,4] #,3] #,4,5,6,7,8] #,9,10]
H = [1,2,3,4,5,6,7,8] #,32,128,512]
max_l = 10

import itertools
import numpy
import random
from sklearn.neural_network import MLPClassifier

results = []
print("n","k","h","successful classifications", "rate")
for k in K:
    numpy.random.seed(0)
    for h in H:
        numpy.random.seed(0)
        for n in range(N):
            n += 1
            if n <= 2*(h-1)*(k-1)+k+1:
              continue
            data_results = []
            l_len = min(n-1,max_l-1)
            for r_data in range(20):
                numpy.random.seed(r_data)
                data = numpy.random.normal(size=[N,k])
                numpy.random.seed(0)
                true_results = 0
                for label_int in range(2**l_len):
                    if max_l < n:
                      label_int = random.randint(0, 2**(n-1))
                    labels = [int(i) for i in bin(label_int * 2 + 2**(N+2))[-n:]]
                    d = data[:n]
                    converged = False
                    for r_mlp in range(20): #lbfgs
                        clf = MLPClassifier(
                            hidden_layer_sizes=(h,), random_state=r_mlp, 
                            #activation='relu', solver="lbfgs",
                            activation='relu', solver="lbfgs",
                            alpha=0)
                        clf.fit(d, labels)
                        if (clf.predict(d) == labels).all():
                            true_results += 1
                            converged = True
                            break
                    if true_results >= 2**(l_len-1):
                      break
                if true_results >= 2**(l_len-1):
                    data_results.append(true_results)
                    break
                if data_results and true_results > max(data_results):
                    print(n, k, h, true_results, true_results*1.0/2**l_len, "intermediate", r_data, max(data_results)*1.0/2**l_len)
                data_results.append(true_results)
            true_results = max(data_results)
            print(n, k, h, true_results, true_results*1.0/2**l_len)
            results.append((n, k, h, true_results, true_results*1.0/2**l_len))
            if true_results*1.0/2**l_len < 0.45:
                print "KVC(0.95): "+str((n-1,k, h))
                print
                break
    print "done"