test_X = test_data['inputs']
test_y = test_data['targets']
#normalize the value of features to be between -1 to 1
for j in xrange(test_X.shape[1]):
test_X[:,j] = (test_X[:,j] - train_X_features_mean[0,j])/train_X_features_range[0,j]
# Run classifier
model = None
params = json.loads(params_json)
# Standard algorithms
if not ensemble_method:
# KNN
if model_name == 'knn':
model = KNNModel(params) # KNNModel({'k': 10})
# Logistic
elif model_name == 'logistic':
model = LogisticRegressionModel(params) # LogisticRegressionModel({'penalty': 'l2', 'regularization_term': 0.1})
# SVM
elif model_name == 'svm':
model = SVMModel(params) # SVMModel({'kernel': 'rbf', 'probability_flag': False})
# MoG
elif model_name == 'mog':
model = MOGModel(params) # MOGModel({'n_components': 20})
# Multi-layer perceptron (NNets)
elif model_name == 'neural_net':
from models import SVMModel, KNNModel
from util import read_data, number_of_inputs
data = read_data('training_data/walls.csv')
# divide loaded data to inputs and outputs
X = data[:, :number_of_inputs]
y = data[:, -1]
model = SVMModel()
model.fit(X, y)
model.save('model/walls_svm.pkl')
model = KNNModel()
model.fit(X, y)
model.save('model/walls_knn.pkl')
valid_X = valid_data['inputs']
valid_y = valid_data['targets']
#normalize the value of features to be between -1 to 1
for j in xrange(valid_X.shape[1]):
valid_X[:,j] = (valid_X[:,j] - train_X_features_mean[0,j])/train_X_features_range[0,j]
# Run classifier
model = None
params = json.loads(params_json)
# Standard algorithms
if not ensemble_method:
# KNN
if model_name == 'knn':
model = KNNModel(params) # KNNModel({'k': 10})
# Logistic
elif model_name == 'logistic':
model = LogisticRegressionModel(params) # LogisticRegressionModel({'penalty': 'l2', 'regularization_term': 0.1})
# SVM
elif model_name == 'svm':
model = SVMModel(params) # SVMModel({'kernel': 'rbf', 'probability_flag': False})
# MoG
elif model_name == 'mog':
model = MOGModel(params) # MOGModel({'n_components': 20})
# Multi-layer perceptron (NNets)
elif model_name == 'neural_net':
