def train(self): self.net = pyrb.train_ols(self.data, self.target, 10e-8, 0.8, verbose=True) S = self.net.sim(self.data) errors = 0 count = 0 for i in range(len(self.target)): count += 1 if int(round(S[i][0])) != self.target[i]: errors += 1
plt.legend(bbox_to_anchor=(1, 1), bbox_transform=plt.gcf().transFigure)
plt.show()
def plotCityMap(crimes):
return None
if __name__ == "__main__":
start = time.time()
[data, target] = importDataFromFile("train.csv", [ 0, 7, 8 ], 1)
[data, target] = [np.array(data).reshape(len(data), 3), np.array(target).reshape(len(target), 1)]
net = pyrb.train_ols(data, target, 10e-8, 0.8, verbose=True)
S = net.sim(data)
errors = 0
count = 0
for i in range(len(target)):
count += 1
if int(round(S[i][0])) != target[i]:
errors += 1
end = time.time()
print('Learning error: ' + str(round((errors / count) * 100)) + '%')
print('Time: ' + str(end - start) + ' seconds')
#Evaluates function over every point of the grid V = heart(P[0:1], P[1:]) #Plot plt.plot(P[0:1][V<0.2], P[1:][V<0.2], '*r', P[0:1][V>=0.2], P[1:][V>=0.2], 'o') plt.show() #defines an exact RBFN enet = pyrb.train_exact(P.T, V.T, 0.3) #simulate S = enet.sim(P.T).T plt.plot(P[0:1][S<0.2], P[1:][S<0.2], '*r', P[0:1][S>=0.2], P[1:][S>=0.2], 'o') plt.show() # small differences are due to ill conditioning #What if we compute points outside training set O=np.random.uniform(size=(2,5000), low=-2., high=2.) S = enet.sim(O.T).T plt.plot(O[0:1][S<0.2], O[1:][S<0.2], '*r', O[0:1][S>=0.2], O[1:][S>=0.2], 'o') plt.show() #To achieve better generalization we can train an inexact RBFN inet = pyrb.train_ols(P.T, V.T, 0.0007, 0.3, verbose=True) #simulate S = inet.sim(P.T).T plt.plot(P[0:1][S<0.2], P[1:][S<0.2], '*r', P[0:1][S>=0.2], P[1:][S>=0.2], 'o') plt.show() #Outside training set... S = inet.sim(O.T).T plt.plot(O[0:1][S<0.2], O[1:][S<0.2], '*r', O[0:1][S>=0.2], O[1:][S>=0.2], 'o') plt.show()
if len(sys.argv) >= 3:
arg1 = float(sys.argv[1])
arg2 = float(sys.argv[2])
else:
arg1 = 0.1
arg2 = 1
TRAIN_SIZE = SIZE - ACCELERATION_FACTOR
EVAL_SIZE = SIZE - TRAIN_SIZE
train_label = np.reshape(train_label, [TRAIN_SIZE, 1])
eval_label = np.reshape(eval_label, [EVAL_SIZE, 1])
print ("training ")
rbf = pyrb.train_ols(train_data, train_label, mse=arg1, gw=arg2, verbose=True)
# rbf = pyrb.train_exact(train_data, train_label, 0.4)
print ("train done")
train_result = rbf.sim(train_data)
train_result = np.reshape(train_result, [-1, 1])
x = np.sum(train_result == train_label)
train_acc = np.sum(train_result == train_label) / float(TRAIN_SIZE)
eval_result = rbf.sim(eval_data)
eval_result = np.reshape(eval_result, [-1, 1])
eval_acc = np.sum(eval_result == eval_label) / float(EVAL_SIZE)
out_train = train_result - train_label
for x in range(TRAIN_SIZE):
fake_train_label[x, train_label[x]] = 1
for x in range(EVAL_SIZE):
fake_eval_label[x, eval_label[x]] = 1
"""
# train_label = fake_train_label
# eval_label = fake_eval_label
TRAIN_SIZE = SIZE - ACCELERATION_FACTOR
EVAL_SIZE = SIZE - TRAIN_SIZE
train_label = np.reshape(train_label, [TRAIN_SIZE, 1])
eval_label = np.reshape(eval_label, [EVAL_SIZE, 1])
rbf = pyrb.train_ols(train_data, train_label, mse=MSE, gw=GW, verbose=True)
# rbf = pyrb.train_exact(train_data, train_label, GW)
print ("train done")
train_result = rbf.sim(train_data).astype(np.int)
train_result = np.reshape(train_result, [-1, 1])
x = np.sum(train_result == train_label)
train_acc = np.sum(train_result == train_label) / float(TRAIN_SIZE)
eval_result = rbf.sim(eval_data).astype(np.int)
eval_result = np.reshape(eval_result, [-1, 1])
eval_acc = np.sum(eval_result == eval_label) / float(EVAL_SIZE)
