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We will also need to normalize the input data, so we create a function to do so:
Chapter 10
def normalize(df):
result = df.copy()
for feature_name in df.columns:
max_value = df[feature_name].max()
min_value = df[feature_name].min()
result[feature_name] = (df[feature_name] - min_value) / (max_
value - min_value)
return result.astype(np.float32)
Let us read the data. The data contains Red, Green, and Blue channel values. Let us
normalize them:
## Reading input data from file
import pandas as pd
df = pd.read_csv('colors.csv') # The last column of data file is a
label
data = normalize(df[['R', 'G', 'B']]).values
name = df['Color-Name'].values
n_dim = len(df.columns) - 1
# Data for Training
colors = data
color_names = name
Let us create our SOM and fit it:
som = WTU(30, 30, n_dim, 400, sigma=10.0)
som.fit(colors)
Now, let's look at the result of the trained model. In the following code, you can see
the color map in the 2D neuron lattice:
# Get output grid
image_grid = som.get_centroids()
# Map colours to their closest neurons
mapped = som.map_vects(colors)
# Plot
plt.imshow(image_grid)
plt.title('Color Grid SOM')
for i, m in enumerate(mapped):
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