def main():
fd = function_data()
recogniter = FunctionRecogniter()
# トレーニング
# learn_layer = ['region1', 'region2', 'region3']
#for learn_layer in [['region1', 'region2'], ['region1', 'region2']]:
for learn_layer in [['region1'], ['region2'],]:
for i in range(25):
print i,
for num, ftype in enumerate(fd.function_list.keys()):
data = fd.get_data(ftype)
for x, y in data:
input_data = {
'xy_value': [x, y],
'x_value': x,
'y_value': y,
'ftype': ftype
}
inferences = recogniter.run(input_data, learn=True, learn_layer=learn_layer)
# print
recogniter.print_inferences(input_data, inferences)
recogniter.reset()
# 予測
predict_example(fd, recogniter)
#predict_example_2(fd, recogniter)
predict_example_3(fd, recogniter)
def main():
type_a_data = [2] * 10 + [50] * 30
type_b_data = [1] * 10 + [50] * 30
data_set = {'a': type_a_data, 'b': type_b_data}
recogniter = FunctionRecogniter()
plotter = Plotter()
result = defaultdict(list)
plotter.initialize(
{
'anomaly': {
'ylim': [0, 1],
'sub_title': ['a', 'b']
},
'output-differ': {
'ylim': [0, 1],
'sub_title': ['a', 'b']
},
'first': {
'ylim': [0, 100],
'sub_title': ['a', 'b']
},
},
movable=True)
# トレーニング
for i in range(100):
anomaly_mean = {}
output_differ_mean = {}
first_input = {}
for ftype, data in data_set.items():
tmp_anomaly = []
tmp_output_differ = []
first_input_cnt = 0
for x, y in enumerate(data):
input_data = {
'xy_value': [x, y],
'x_value': x,
'y_value': y,
'ftype': ftype
}
inferences = recogniter.run(input_data, learn=True)
# for plot
tmp_anomaly.append(inferences['anomaly']['region1'])
if len(inferences["output_differ"]) == 0:
first_input_cnt += 1
else:
tmp_output_differ.append(
inferences["output_differ"]['region1'])
# print
#recogniter.print_inferences(input_data, inferences)
recogniter.reset()
# for plot
anomaly_mean[ftype] = sum(tmp_anomaly) / len(tmp_anomaly)
output_differ_mean[ftype] = sum(tmp_output_differ) / len(
tmp_output_differ)
first_input[ftype] = first_input_cnt
plotter.write_draw(title='anomaly',
x_value={
'a': i,
'b': i
},
y_value=anomaly_mean)
plotter.write_draw(title='output-differ',
x_value={
'a': i,
'b': i
},
y_value=output_differ_mean)
plotter.write_draw(title='first',
x_value={
'a': i,
'b': i
},
y_value=first_input)
# 予測
#if i % 10 == 0:
print
print '##################### ', i
predict_example_4(data_set, recogniter)
plotter.show()
def main():
type_a_data = [2] * 10 + [50] * 30
type_b_data = [1] * 10 + [50] * 30
data_set = {'a': type_a_data, 'b':type_b_data}
recogniter = FunctionRecogniter()
plotter = Plotter()
result = defaultdict(list)
plotter.initialize({
'anomaly':{
'ylim': [0,1],
'sub_title': ['a', 'b']},
'output-differ':{
'ylim': [0,1],
'sub_title': ['a', 'b']},
'first':{
'ylim': [0,100],
'sub_title': ['a', 'b']},
}, movable=True)
# トレーニング
for i in range(100):
anomaly_mean = {}
output_differ_mean = {}
first_input = {}
for ftype, data in data_set.items():
tmp_anomaly = []
tmp_output_differ = []
first_input_cnt = 0
for x, y in enumerate(data):
input_data = {
'xy_value': [x, y],
'x_value': x,
'y_value': y,
'ftype': ftype
}
inferences = recogniter.run(input_data, learn=True)
# for plot
tmp_anomaly.append(inferences['anomaly']['region1'] )
if len(inferences["output_differ"]) == 0:
first_input_cnt += 1
else:
tmp_output_differ.append(inferences["output_differ"]['region1'])
# print
#recogniter.print_inferences(input_data, inferences)
recogniter.reset()
# for plot
anomaly_mean[ftype] = sum(tmp_anomaly) / len(tmp_anomaly)
output_differ_mean[ftype] = sum(tmp_output_differ) / len(tmp_output_differ)
first_input[ftype] = first_input_cnt
plotter.write_draw(title='anomaly', x_value={'a':i, 'b':i}, y_value=anomaly_mean)
plotter.write_draw(title='output-differ', x_value={'a':i, 'b':i}, y_value=output_differ_mean)
plotter.write_draw(title='first', x_value={'a':i, 'b':i}, y_value=first_input)
# 予測
#if i % 10 == 0:
print
print '##################### ', i
predict_example_4(data_set, recogniter)
plotter.show()
