# kmeans.distribute() # error = kmeans.check() # print('separated sets normalize') # print("error is %f%%" % (error*100)) # # print('none separated sets') # points = Points() # points.init(file_name="sets_connected.xls", start_row=0, dim=5) # kmeans = Kmeans(points=points.points, centroid_num=5) # kmeans.distribute() # error = kmeans.check() # print('none separated sets') # print("error is %f%%" % (error*100)) # # print('none separated sets normalize') # points = Points() # points.init(file_name="sets_connected_norma.xls", start_row=0, dim=5) # kmeans = Kmeans(points=points.points, centroid_num=5) # kmeans.distribute() # error = kmeans.check() # print('none separated sets normalize') # print("error is %f%%" % (error*100)) points = Points() points.init(file_name="iris.xls", start_row=0, end_row=2, dim=4) prc = Pca(points=points.points) prc.distribute() error = prc.check() print("error is %f%%" % (error*100))
from model import Points # restore network # with open("my_network", 'rb') as pickle_file: # network = pickle.load(pickle_file) # init new network network = NeuralNetwork(inputs=5, outs=1, hidden_layers_num=5, layer_neurons_num=5) # simple check that everithing is working # matr = np.random.rand(5, 1) # print("matr input is: ", matr) # result = network.work(matr) # print("result is : ", result) # get data from xl points = Points() points.init(file_name="separated_sets.xls") # study our network for i in range(100): error = 0 for point in points.points: error += abs(network.study(point.cords, np.array([point.set_class]))) print(error) # save network with open("my_network", 'wb') as pickle_file: pickle.dump(network, pickle_file)