main.py

This AI Model is making a random pie chart from csv

piechartonai.py

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+# -*- coding: utf-8 -*-
+"""PiechartOnAI.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/17oqp758ffviqvK2q7mzgXY0VOJN6WLET
+"""
+
+import tensorflow as tf
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+df = pd.read_csv('test1.csv')
+slices = df['Slices']
+randomness = df['Randomness']
+
+from sklearn.preprocessing import MinMaxScaler
+scaler = MinMaxScaler()
+slices_norm = scaler.fit_transform(slices.values.reshape(-1, 1))
+randomness_norm = scaler.fit_transform(randomness.values.reshape(-1, 1))
+
+# Define the model
+inputs1 = tf.keras.layers.Input(shape=(1,))
+inputs2 = tf.keras.layers.Input(shape=(1,))
+x1 = tf.keras.layers.Dense(8, activation='relu')(inputs1)
+x2 = tf.keras.layers.Dense(8, activation='relu')(inputs2)
+x = tf.keras.layers.Concatenate()([x1, x2])
+output = tf.keras.layers.Dense(1, activation='sigmoid')(x)
+# Generate the target value
+y = slices_norm + randomness_norm
+y = y / np.sum(y)
+
+model = tf.keras.models.Model(inputs=[inputs1, inputs2], outputs=output)
+model.compile(loss='mse', optimizer='adam')
+
+# Train the model
+history = model.fit([slices_norm, randomness_norm], y, epochs=100, batch_size=32)
+
+# Define the input values for the pie chart
+slices_input = np.array([[0.25]])
+randomness_input = np.array([[0.75]])
+
+# Use the trained model to predict the target value
+prediction = model.predict([slices_input, randomness_input])
+prediction = prediction[0][0]
+
+# Generate the pie chart using the predicted target value
+labels = ['Elfogultságok','Vesztség','Súlyok','Véletlenszerűség']
+sizes = [slices_input[0][0]*prediction*100, slices_input[0][0]*(1-prediction)*100, (1-slices_input[0][0])*prediction*100, (1-slices_input[0][0])*(1-prediction)*100]
+explode = (0, 0, 0, 0.1)
+fig1, ax1 = plt.subplots()
+ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', shadow=True, startangle=90)
+ax1.axis('equal')
+plt.show()
+print("Véletlenszerűség (mennyire véletlenszerű az előrejelzés)")
+print("Veszteség (Ha a veszteség nagy, az azt jelenti, hogy a tévedés nagy, különben a tévedés kicsi")
+print("Súlyok (mennyit ér a tévedés az egyes neuronokon)")
+print("Elfogultságok (Milyen jó az előrejelzés)")
+