GPU glucose2

import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier

# Define the blood test results for six persons
person1_results = [80, 90, 100, 110, 120, 0]
person2_results = [95, 105, 90, 115, 100, 1]
person3_results = [110, 95, 85, 75, 120, 0]
person4_results = [75, 80, 85, 90, 95, 1]
person5_results = [105, 115, 100, 105, 110, 0]
person6_results = [90, 110, 115, 95, 120, 1]

# Combine the results into a list
all_results = [person1_results, person2_results, person3_results, person4_results, person5_results, person6_results]

# Extract the last column (0 or 1) as labels
labels = [results[-1] for results in all_results]

# Remove the last column from the dataset
data = [results[:-1] for results in all_results]

# Define the number of rows for testing and learning
num_testing_rows = 4

# Split the data into training and testing datasets
X_train, X_test, y_train, y_test = data[:-num_testing_rows], data[-num_testing_rows:], labels[:-num_testing_rows], labels[-num_testing_rows:]

# Train a machine learning model (Random Forest classifier)
clf = RandomForestClassifier()
clf.fit(X_train, y_train)

# Use the trained model to predict the last column for the testing dataset
y_pred = clf.predict(X_test)

# Set colors for gray, green, and red
gray_color = (128, 128, 128)  # Gray as (128, 128, 128) in RGB
green_color = (0, 128, 0)  # Green as (0, 128, 0) in RGB
red_color = (255, 0, 0)  # Red as (255, 0, 0) in RGB

# Create an empty array for the first image (based on given data)
image1 = np.zeros((len(data), len(data[0]) + 1, 3), dtype=np.uint8)

# Populate image1 with gray and green colors based on the labels in the last column
for i, label in enumerate(labels):
    for j in range(len(all_results[0])):
        pixel_value = int(np.interp(all_results[i][j], [min(all_results[i]), max(all_results[i])], [0, 255]))
        image1[i, j] = np.array([pixel_value] * 3)
    if label == 0:
        image1[i, -1] = np.array([green_color])
    elif label == 1:
        image1[i, -1] = np.array([red_color])

# Use the trained model to predict the labels for the test data
y_pred = clf.predict(X_test)

# Create an empty array for the second image (based on testing)
image2 = np.zeros((len(X_test), len(all_results[0]), 3), dtype=np.uint8)

# Populate image2 with gray and red/green colors based on the predicted labels
for i, label in enumerate(y_pred):
    for j in range(len(all_results[0])):
        pixel_value = int(np.interp(all_results[i][j], [min(all_results[i]), max(all_results[i])], [0, 255]))
        image2[i, j] = np.array([pixel_value] * 3)
    if label == 0:
        image2[i, -1] = np.array([green_color])
    elif label == 1:
        image2[i, -1] = np.array([red_color])

# Display both images side by side
plt.figure(figsize=(12, 5))
plt.subplot(1, 2, 1)
plt.imshow(image1)
plt.title("Learning Data")
plt.axis("off")

plt.subplot(1, 2, 2)
plt.imshow(image2)
plt.title("Testing Data")
plt.axis("off")

plt.show()