def separable():
print("----------------separable-----------------------")
mat = Arff("./separableIsSquare.arff", label_count=1)
np_mat = mat.data
data = mat[:, :-1]
labels = mat[:, -1].reshape(-1, 1)
print(data[:, 1])
print(labels)
### Make the Classifier #####
P3Class = None
for lr in range(10, 0, -1):
P3Class = PerceptronClassifier(lr=0.1*lr, shuffle=False)
P3Class.fit(data, labels, standard_weight_value=None)
Accuracy = P3Class.score(data, labels)
print("Learning Rate = ", 0.1*lr)
print("Accuracy = [{:.2f}]".format(Accuracy))
print("Epochs = ", P3Class.get_epochs_trained())
# print(P3Class)
## could not get graphing to work in time...
# graph(data[:, 0], data[:, 1], labels=mat[:, -1])
w = P3Class.get_weights()
y = lambda x: (-w[0]/w[1])*x - (w[2]/w[1])
grapher = Grapher()
grapher.graph(data[:, 0], data[:, 1], labels=mat[:, -1], title="Separable")
grapher.add_function(y)
grapher.show("separable.svg")
def inseparable():
print("----------------Inseparable-----------------------")
mat = Arff("./impossible.arff", label_count=1)
np_mat = mat.data
data = mat[:, :-1]
labels = mat[:, -1].reshape(-1, 1)
### Make the Classifier #####
P4Class = None
for lr in range(10, 0, -1):
P4Class = PerceptronClassifier(lr=0.1*lr, deterministic=10, shuffle=False)
P4Class.fit(data, labels, standard_weight_value=None)
Accuracy = P4Class.score(data, labels)
print("Learning Rate = ", 0.1*lr)
print("Accuracy = [{:.2f}]".format(Accuracy))
print("Epochs = ", P4Class.get_epochs_trained())
w = P4Class.get_weights()
y = lambda x: (-w[0]/w[1])*x - (w[2]/w[1])
grapher = Grapher()
grapher.graph(data[:, 0], data[:, 1], labels=mat[:, -1], title="Inseparable")
grapher.add_function(y)
grapher.show("Inseparable.svg")
def voting():
print("--------------voting---------------------")
mat = Arff("../data/perceptron/vote.arff", label_count=1)
np_mat = mat.data
avg = []
for iteration in range(5):
print("xxxxxxxxxxx " + str(iteration) + " xxxxxxxx")
training, testing = _shuffle_split(mat.data, .3)
data = training[:, :-1]
labels = training[:, -1].reshape(-1, 1)
P5Class = PerceptronClassifier(lr=0.1, shuffle=True)
P5Class.fit(data, labels)
Accuracy = P5Class.score(data, labels)
print("Accuracy = [{:.2f}]".format(Accuracy))
print("Epochs = ", P5Class.get_epochs_trained())
tData = testing[:, :-1]
tLabels = testing[:, -1].reshape(-1, 1)
tAccuracy = P5Class.score(tData, tLabels)
print("Test Accuracy = [{:.2f}]".format(tAccuracy))
weights = P5Class.get_weights()
print(weights)
sort_weights = sorted(zip(weights, list(range(len(weights)))), key=lambda x: abs(x[0]), reverse=True)
print("sorted:\r\n", sort_weights)
scores = P5Class.getTrace().getColumns("epochScore")
print('scores', scores)
avg.append((float(scores[-2][0]) - float(scores[0][0])) / len(scores))
print('avg', avg)
grapher = Grapher()
grapher.graph(list(range(len(avg))), avg, labels=[1]*len(avg), points=False, title="Average Scores", xlabel="Iteration", ylabel="score")
grapher.show("AverageScores.svg")
