def chart_job(s, i, j):
learning_data, testing_data = load_wine(test_count=20)
net = nn.NeuralNetwork(0.01, 100, [*s], 1.04, 1.05, 0.7,
0.020) # int((s[0]*s[1])*5)
net.feed_training_data(*learning_data)
net.feed_test_data(*testing_data)
net.start_learning()
prediction, cost = net.test(*testing_data)
return (i, j, cost, s)
def main():
args = parse_arguments()
#1. Load the data (Iris Set)
#train_set, test_set = dl.load_diab()
#train_set, test_set = dl.load_susy()
#train_set, test_set = dl.load_iris()
train_set, test_set = dl.load_wine()
Xtrain = train_set[0]
ytrain = train_set[1]
Xtest = test_set[0]
ytest = test_set[1]
# dl.load_wine()
#Learn the weights
#weights = pa.fit(Xtrain, ytrain, float(sys.argv[1]))
#yhat = pa.predict(Xtest, weights)
#weights = sgd.fit(Xtrain, ytrain, int(sys.argv[1]))
#yhat = sgd.predict(Xtest, weights)
#algs = {'batch': pa.BatchGradientDescent(), 'stochastic': pa.StochasticGradientDescent()}
#algs = {'logist': pa.LogisticRegression()}
algs = {'softmax': pa.SoftMaxRegression()}
params = {
'tolerance': args.tolerance,
'epoch': args.epoch
} #'steps': args.steps}
#use .format for printing
for learner_name, learner in algs.iteritems():
print(learner_name, learner)
learner.reset(params)
learner.fit(Xtrain, ytrain)
yhat = learner.predict(Xtest)
print('yhat: {}'.format(yhat))
print('y: {}'.format(ytest))
print(accuracy_score(ytest, yhat))
#print(learner.err)
#plt.plot(learner.err)
#plt.show()
from sklearn.linear_model import LogisticRegression
lg = LogisticRegression(multi_class='multinomial',
solver='newton-cg',
random_state=42,
verbose=1,
max_iter=1000,
penalty="l2")
lg.fit(Xtrain, ytrain)
yhat = lg.predict(Xtest)
print("----" * 50)
print("Testing scikit logistic regression")
print('yhat: {}'.format(yhat))
print('y: {}'.format(ytest))
print(accuracy_score(ytest, yhat))
import matplotlib.pyplot as plt
import numpy as np
import neural_network as nn
from data_loader import load_wine
if __name__ == "__main__":
net = nn.NeuralNetwork(None, None, None, None, None, None, None)
net.load_model('models/000100_10_6.mdl')
plt.figure()
for i in range(12):
P, T = load_wine(test_count=20)[1]
prediction, cost = net.test(P, T)
plt.subplot(3, 4, i + 1)
plt.grid(linestyle='--')
plt.yticks(np.arange(min(T), max(T) + 0.01, 0.25))
plt.plot(prediction)
plt.plot(T)
plt.title(f'Cost: {cost:.8f}')
plt.show()
if __name__ == "__main__":
# a, b = range(10, 101, 20), range(10, 101, 20)
a, b = range(2, 10), range(2, 10)
S1, S2 = np.meshgrid(a, b)
c, t = 1, len(a) * len(b)
PK = np.empty(S1.shape, dtype=int)
plt.figure()
ax = plt.axes(projection='3d')
ax.set_xlabel('S1')
ax.set_ylabel('S2')
ax.set_zlabel('%PK')
ax.set_zlim(0, 100)
learning_data, testing_data = load_wine(test_count=20)
start = time()
for i, row in enumerate(zip(S1, S2)):
for j, s in enumerate(zip(*row)):
while True:
learning_data, testing_data = learning_data.copy(
), testing_data.copy()
net = nn.NeuralNetwork(0.01, int(s[0] * s[1] / 4), [*s], 1.04,
1.05, 0.7, 0.020)
net.feed_training_data(*learning_data)
net.feed_test_data(*testing_data)
net.start_learning(live_text=True)
prediction = net.test(*testing_data)[0]
pk = [
# Calculate and Accumulate accuracy
acc_mdc = evaluation.calculate_accuracy(Yts, Yh)
mean_acc_mdc = mean_acc_mdc + acc_mdc
# Mean Accuracy
mean_acc_mdc = mean_acc_mdc / N
print(
'Mean Accuracy of MD Classifier, with Iris Dataset and Leave-one-out Method:'
)
print(mean_acc_mdc)
# ########################## Experiment 2 - Wine, KNN, Random Subsampling
# Load DataSet
X, Y = data_loader.load_wine()
# Define number of realizations (for random subsampling)
Nrealizations = 10
# Init Output
mean_acc_knn = 0
for i in range(Nrealizations):
# Split data between train and test
Xtr, Ytr, Xts, Yts = hold_out.random_subsampling(X,
Y,
train_size=0.8,
random_state=None)