def perceptronModel():
try:
x_train, y_train, isBiased, learningRate, epochNum, x_test, y_test = modelOperations(
'perceptron')
W = perceptron.train(x_train, y_train, isBiased, learningRate,
epochNum)
labels = [firstClassCB.get(), secondClassCB.get()]
perceptron.test(x_test, y_test, W, labels)
# show confusion matrix and accuracy
except:
pass
def simulate_skin(steps=5, max_iter=100, learning_rate=0.1):
"""Simulate learning skin data set."""
data = read_data('Skin_NonSkin.txt')
train_data, test_data = split_list(data, 0.75)
start = len(train_data)/steps # First step training set size.
end = len(train_data) # Final step training set size.
sizes = [] # Training data set sizes.
success = [] # Success rates according to training data set sizes.
for i in xrange(steps):
# Increase training data size according to iteration.
size = start + i*end/steps
current_train_data = train_data[:size]
w = train(current_train_data, max_iter=max_iter, r=learning_rate)
error = test(test_data, w)
status(current_train_data, test_data, error)
print
# Record size-success statistics.
sizes.append(size)
success.append(100 - error)
plot_success_per_size(sizes, success)
show()
def simulate_seperable(data_size):
"""Simulate learning a completely seperable data set."""
data = generate_sphere_data(10000, margin=0)
train_data, test_data = split_list(data, 0.75)
w = train(train_data, max_iter=500, r=0.01)
error = test(test_data, w)
status(train_data, test_data, error)
plot_data(data)
plot_w(data, w)
show()
def simulate_increasing(data_size, margin=0.3, max_iter=100, learning_rate=0.1,
steps=5, start=None, end=None):
"""Simulate learning an increasing training data set.
Generates an unseperable data set, and trains on an increasing training
set, then tests and plots.
start: Initial (first step) training data set size.
end: Final (last step) training data set size.
"""
data = generate_sphere_data(data_size, margin=margin)
train_data, test_data = split_list(data, 0.75)
# Initialize start/end sizes if not given.
start = len(train_data)/steps if start is None else start
end = len(train_data) if end is None else end
w_colors = ['b', 'c', 'm', 'y', 'k'] # w vector (line) graph color.
w_gs = [] # w plot graphs.
sizes = [] # Training data set sizes.
success = [] # Success rates according to training data set sizes.
for i in xrange(steps):
# Increase training data size according to iteration.
size = start + i*end/steps
current_train_data = train_data[:size]
w = train(current_train_data, max_iter=max_iter, r=learning_rate)
error = test(test_data, w)
status(current_train_data, test_data, error)
print
# Record size-success statistics.
sizes.append(size)
success.append(100 - error)
# Plot decision boundary.
w_color = w_colors[i] if i < len(w_colors) else w_colors[-1]
figure(0)
g, = plot_w(current_train_data, w, color=w_color)
w_gs.append(g)
figure(0).suptitle('Test data size: %d\nMaximum iterations: %d' % (len(test_data), max_iter))
plot_w_legend(w_gs, sizes)
plot_data(data)
figure(1).suptitle('Success rate according to training set size.')
plot_success_per_size(sizes, success)
show()
def main():
training = read_data(argv[1])
test = read_data(argv[2])
filter_modes = ["unfiltered", "filtered"]
iteration_limits = [10, 25, 50, 100]
training_rates = [0.01, 0.05, 0.1, 0.5, 0.8]
for mode in filter_modes:
for limit in iteration_limits:
for rate in training_rates:
weights = perceptron.train(training[mode], limit, rate)
accuracy = perceptron.test(test[mode], weights) * 100
print("{0:.6f}".format(accuracy) + "% accurate:", mode,
"stop words,", limit, "iterations,", rate,
"training rate")
import perceptron
import csvconv
#inputs
lr = float(input("Input Learning Rate:"))
Epochs = int(input("Input Number Of Epochs:"))
#training
floatdata = csvconv.tofloat('train.csv')
weights = perceptron.train(floatdata, lr, Epochs)
print("Weights\n ", weights)
#testing
floattest = csvconv.tofloat('test.csv')
score = perceptron.test(floattest, weights)
print('Score=', score, '/', len(floattest))
acc = (score / len(floattest)) * 100
print("Accuracy=", acc)
X_train, Y_train = reviews_to_features(training_set)
print("Featurized training data.")
weights, losses = perceptron.train(X_train,
Y_train,
iterations=ITERATIONS,
eta=ETA)
print("Done training.")
X_test, Y_test = reviews_to_features(dev_set)
print("Featurized test data.")
test_scores = perceptron.score(X_test.T, weights)
test_sentiments = perceptron.predict(test_scores)
(accuracy, recall, precision, f1, false_positive_rate,
false_negative_rate) = perceptron.test(Y_test, test_sentiments)
print(
f"Predicted scores w/ threshold = 0.5, iterations = {ITERATIONS}, eta = {ETA}:"
)
print(f" - accuracy : {accuracy}")
print(f" - recall : {recall}")
print(f" - precision : {precision}")
print(f" - f1 : {f1}")
print(f" - fpr : {false_positive_rate}")
print(f" - fnr : {false_negative_rate}")
if ENABLE_ROC:
fpr = []
fnr = []
thresholds = list(np.round(np.linspace(0, 1, 101), decimals=2))
for threshold in thresholds:
import random, numpy, csv
import perceptron
def load_csv(filename):
file = open(filename, "rb")
lines = csv.reader(file)
dataset = list(lines)
random.shuffle(dataset)
return dataset
inputs = load_csv("iris.csv")
weights = [numpy.random.random_sample() for i in range(len(inputs))]
bias = 0
learning_rate = 0.01
weights = perceptron.train(inputs, weights, bias, learning_rate)
c = perceptron.test(inputs, weights, bias)
print c
parser.add_argument(
"-p",
"--pedestrian",
help=
"a pedestrian is present - add this tag if the image contains a pedestrian and omit the tag if it does not",
action="store_true")
parser.add_argument("-w",
"--weights",
help="input file for weights (pickled)",
required=True)
args = parser.parse_args()
# Load image, get HOG
whole_hog = hog.hog(Image.open(args.image))
weights = None
# Open weights
with open(args.weights, 'rb') as f:
weights = pickle.load(f)
# Use perceptron
result = perceptron.test(whole_hog, weights)
if result == args.pedestrian:
print("Success")
else:
print("Failure")
exit(0 if result == args.pedestrian else 1)