Added initial code.

thm/aoc23/day14/detector.py

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+#These are the imports that we need for our Neural Network
+#Numpy is a powerful array and matrix library used to format our data
+import numpy as np
+#Pandas is a machine learning library that also allows for reading and formatting data structures
+import pandas as pd
+#This will be used to split our data
+from sklearn.model_selection import train_test_split
+#This is used to normalize our data
+from sklearn.preprocessing import StandardScaler
+#This is used to encode our text data to integers
+from sklearn.preprocessing import LabelEncoder
+#This is our Multi-Layer Perceptron Neural Network
+from sklearn.neural_network import MLPClassifier
+
+#These are the colour labels that we will convert to int
+colours = ["Red", "Blue", "Green", "Yellow", "Pink", "Purple", "Orange"]
+
+
+#Read the training and testing data files
+training_data = pd.read_csv("training_dataset.csv")
+training_data.head()
+
+testing_data = pd.read_csv("testing_dataset.csv")
+testing_data.head()
+
+#The Neural Network cannot take Strings as input, therefore we will encode the strings as integers
+encoder = LabelEncoder()
+encoder.fit(training_data["Colour Scheme"])
+training_data["Colour Scheme"] = encoder.transform(training_data["Colour Scheme"])
+testing_data["Colour Scheme"] = encoder.transform(testing_data["Colour Scheme"])
+
+#Read the data we will train on
+X = np.asanyarray(training_data[['Height','Width','Length','Colour Scheme','Maker Elf ID','Checker Elf ID']])
+#Read the labels of our training data
+y = np.asanyarray(training_data['Defective'].astype('int'))
+
+#Read our testing data
+test_X = np.asanyarray(testing_data[['Height','Width','Length','Colour Scheme','Maker Elf ID','Checker Elf ID']])
+
+#This will split our training dataset into two with a 80/20 split
+train_X, validate_X, train_y, validate_y = train_test_split(X, y, test_size=0.2)
+
+print ("Sample of our data:")
+print("Features:\n{}\nDefective?:\n{}".format(train_X[:3], train_y[:3]))
+
+#Normalize our dataset
+scaler = StandardScaler()
+scaler.fit(train_X)
+
+train_X = scaler.transform(train_X)
+validate_X = scaler.transform(validate_X)
+test_X = scaler.transform(test_X)
+
+print ("Sampe of our data after normalization:")
+print("Features:\n{}\nDefective?:\n{}".format(train_X[:3], train_y[:3]))
+
+#Create our classifier
+clf = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(15, 2), max_iter=10000)
+
+print ("Starting to training our Neural Network")
+
+#Train our classifier
+clf.fit(train_X, train_y)
+
+#Validate our Neural Network
+y_predicted = clf.predict(validate_X)
+
+#This function tests how well your Neural Network performs with the validation dataset
+count_correct = 0
+count_incorrect = 0
+for x in range(len(y_predicted)):
+
+    if (y_predicted[x] == validate_y[x]):
+        count_correct += 1
+    else:
+        count_incorrect += 1
+
+print ("Training has been completed, validating neural network now....")
+print ("Total Correct:\t\t" + str(count_correct))
+print ("Total Incorrect:\t" + str(count_incorrect))
+
+accuracy =  ((count_correct * 1.0) / (1.0 * (count_correct + count_incorrect)))
+
+print ("Network Accuracy:\t" + str(accuracy * 100) + "%")
+
+print ("Now we will predict the testing dataset for which we don't have the answers for...")
+
+#Make prediction on the testing data that was not labelled by the elves
+y_test_predictions = clf.predict(test_X)
+
+#This function will save your predictions to a textfile that can be uploaded for scoring
+print ("Saving predictions to a file")
+
+output = open("predictions.txt", 'w')
+
+for value in y_test_predictions:
+    output.write(str(value) + "\n")
+
+print ("Predictions are saved, this file can now be uploaded to verify your Neural Network")
+output.close()