Using the AIRS

Access the Jupyter notebook with the code available here!

Importing the Artificial Immune Recognition System

from aisp.csa import AIRS

Generating dice bubbles for classes randomly.

Using the make_blobs function, two sets of data are generated in the form of bubbles, in the range between 0 and 1, representing each class x and y. Then this data is separated into test and training sets.

from sklearn.datasets import make_blobs
from sklearn.model_selection import train_test_split

# Generating the samples and outputs for the training.
samples, output = make_blobs(n_samples=500 , n_features=2, cluster_std=0.07, center_box=([0.0, 1.0]), centers=[[0.25, 0.75], [0.75, 0.25]], random_state=1234) 
# Separating data for training and testing.
train_x, test_x, train_y, test_y = train_test_split(samples, output, test_size=0.2)

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Testing the model AIRS:

Then, it presents the result of the forecast accuracy.

from sklearn.metrics import confusion_matrix, classification_report, accuracy_score

# Starting the class.
airs = AIRS(seed=1234)
# Carrying out the training:
airs.fit(X=train_x, y=train_y)
# Previewing classes with test samples.
prev_y = airs.predict(test_x)
# Showing the accuracy of predictions for data.
print(f"The accuracy is {accuracy_score(prev_y, test_y)}")
print(classification_report(test_y, prev_y))

Output:

✔ Set of memory cells for classes (0, 1) successfully generated:  ┇██████████┇ 400/400 memory cells for each aᵢ
The accuracy is 1.0
              precision    recall  f1-score   support

           0       1.00      1.00      1.00        51
           1       1.00      1.00      1.00        49

    accuracy                           1.00       100
   macro avg       1.00      1.00      1.00       100
weighted avg       1.00      1.00      1.00       100

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Memory cell and sample plotting: