from som import SOM
if __name__ == "__main__":
a = SOM(5, 5, 3, 2, False, 0.05)
a.train(100,
[[[0, 0, 0], [0, 0]], [[1, 0, 0], [0, 1]], [[0, 1, 0], [1, 0]],
[[1, 1, 1], [1, 1]]])
print("Prediction 0 and 0 ,", round(a.predict([0, 0, 0])[0, 1]))
for col in range(1, num_cols):
col_values.append(input_sheet.col_values(col)[1:])
x = np.array(col_values, dtype='|S4')
y = x.astype(np.float)
maxs = [max(y[col]) for col in range(0, num_cols - 1)]
mins = [min(y[col]) for col in range(0, num_cols - 1)]
data_points = []
for row in range(1, num_rows):
values = []
for col in range(1, num_cols):
values.append(
(float(input_sheet.cell(row, col).value) - mins[col - 1]) /
(maxs[col - 1] - mins[col - 1]))
d = DataPoint(values, int(output_sheet.cell(row, 0).value))
data_points.append(d)
print(num_rows - 1, " points with dimesion=", num_cols - 1, " are added")
return data_points
data_points = load_data("722.xlsx")
s = SOM(2, 8, 27)
s.load_input_data(data_points)
s.fit(2, 0.1)
v = LVQ(2, 6, 5)
v.load_data(data_points)
v.train(5, 0.01, 2)
s.predict(data_points)
v.predict(data_points)
import numpy as np import pandas as pd from som import SOM from sklearn.datasets import load_boston from sklearn.preprocessing import StandardScaler # read in data boston = load_boston().data # standard scale data ss = StandardScaler() X = ss.fit_transform(boston) # instantiate SOM som = SOM(X, 3, 3, 1, 100, 0.01) # train model tree, weights, winners, distances = som.train(X) # print data # print(weights) print(set(winners)) dist, ind = som.predict(X, tree) print(set([x[0] for x in ind]))
sigma = 1
learning_rate = 1
sigma_decrease_rate = 0
som = SOM(inputs,
weights,
sigma=sigma,
learning_rate=learning_rate,
sigma_decrease_rate=sigma_decrease_rate,
epochs=10)
som.train(ignore_hk=True, display=True)
points = [[1, 4], [6, 2], [1, 3], [5, 1], [4, 0], [0, 4], [3, 4]]
predictions = som.predict(points, ignore_hk=True, display=True)
p, m = som.prediction_line(display=True)
def med(points, p, m):
y = []
for point in points:
y.append(m * (point - p[0]) + p[1])
return y
plt.grid()
for point, prediction in zip(points, predictions):
x, y = point
color, label = ("blue",