def report():
report_data = {}
predicted_data = None
standard_data = parser.load_echo_data('data/training_data.csv')
input_data = standard_data.data
test_set = parser.loadTestData('data/data.csv')
try:
classifier_net = ClassifierNeuralNet()
regression_net = RegressorNeuralNet()
classifier_net.train()
regression_net.train()
except Exception as e:
print(e)
try:
data = classifier_net.predictData(input_data)
regressor_data = regression_net.predictData(input_data)
except Exception as e:
print(e)
report_set = []
i = 0
for x in regressor_data:
item={}
item['index'] = str(i)
item['prediction'] = round(x, 2)
item['base'] = standard_data.target[i]
item['probability_differential'] = abs(float(x + 1.0) - float(standard_data.target[i] + 1.0))
i = i + 1
report_set.append(item)
report_data['regressor_results'] = report_set
test_data_results = classifier_net.predictData(test_set.data)
test_report_set = []
for x in test_data_results:
item = {}
item['value'] = x
test_report_set.append(item)
i = 0
report_data['regressor_results'] = test_report_set
test_data_results = regression_net.predictData(test_set.data)
ratios = []
probability_report_set = []
for x in regressor_data:
item={}
item['index'] = str(i)
item['prediction'] = round(x, 2)
item['base'] = data[i]
item['probability_differential'] = round(abs(x - data[i]), 2)
i = i + 1
probability_report_set.append(item)
#Determine predictions for actual test data
class_test_results = classifier_net.predictData(test_set.data)
range_test_results = regression_net.predictData(test_set.data)
i = 0
classifier_data = []
#Iterate through the test set
#Take classifier values
#Take regressor values
#Compare difference
for x in class_test_results:
item={}
item['index'] = str(i)
item['class_value'] = int(x)
item['range_value'] = round(range_test_results[i], 4)
item['deviation'] = round(abs(x - range_test_results[i]), 2)
i = i + 1
classifier_data.append(item)
print(report_data)
return render_template("report.html", regressor_data=report_set, data=classifier_data)
def train(self):
data = parser.load_echo_data('data/training_data.csv')
self.nn.fit(data.data, data.target)
