GPU glucose

import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

# Data preparation in mmol/L
glucose_values_mmol = np.array([5.0, 4.4, 5.3, 4.2, 3.9, 4.7, 5.3, 4.7, 4.4, 3.9, 4.2])

# Data normalization
glucose_normalized = (glucose_values_mmol - np.min(glucose_values_mmol)) / (np.max(glucose_values_mmol) - np.min(glucose_values_mmol))

# Prepare training data
X = glucose_normalized[:-1]
y = glucose_normalized[1:]

# Create a custom RNN-like model
model = Sequential()
model.add(Dense(units=1, input_shape=(1,)))

# Compile the model
model.compile(optimizer='adam', loss='mean_squared_error')

# Reshape the data for training
X = X.reshape(-1, 1)
y = y.reshape(-1, 1)

# Train the model
model.fit(X, y, epochs=100)

# Predict based on previous values
y_pred_normalized = model.predict(X)

# Convert predictions back to the original scale
y_pred = y_pred_normalized * (np.max(glucose_values_mmol) - np.min(glucose_values_mmol)) + np.min(glucose_values_mmol)

# Display the results
x_values = np.arange(1, len(glucose_values_mmol))

plt.plot(x_values, glucose_values_mmol[:-1], marker='o', label='Actual values')
plt.plot(x_values, y_pred, marker='o', label='Prediction')
plt.legend()
plt.show()