scratch for nn

learn_tree.py

@@ -0,0 +1,51 @@
+from collections import Counter
+
+def tree_learn(examples, attributes, default_class):
+    if len(examples) == 0:
+        return default_class
+    
+    if all(examples[0][-1] == example[-1] for example in examples):
+        return examples[0][-1]
+    
+    if len(attributes) == 0:
+        class_counts = Counter(example[-1] for example in examples)
+        majority_class = class_counts.most_common(1)[0][0]
+        return majority_class
+    
+    # Choose the attribute A as the root of the decision tree
+    A = select_attribute(attributes, examples)
+    
+    tree = {A: {}}
+    new_attributes = [attr for attr in attributes if attr != A]
+    new_default_class = Counter(example[-1] for example in examples).most_common(1)[0][0]
+    
+    for value in get_attribute_values(A):
+        new_examples = [example for example in examples if example[attributes.index(A)] == value]
+        subtree = tree_learn(new_examples, new_attributes, new_default_class)
+        tree[A][value] = subtree
+    
+    return tree
+
+# Helper function: Select the best attribute based on a certain criterion (e.g., information gain)
+def select_attribute(attributes, examples):
+    # Implement your attribute selection criterion here
+    pass
+
+# Helper function: Get the possible values of an attribute from the examples
+def get_attribute_values(attribute):
+    # Implement your code to retrieve the attribute values from the examples here
+    pass
+
+# Example usage with coordinates
+examples = [
+    [1, 2, 'A'],
+    [3, 4, 'A'],
+    [5, 6, 'B'],
+    [7, 8, 'B']
+]
+
+attributes = ['x', 'y']
+default_class = 'unknown'
+
+decision_tree = tree_learn(examples, attributes, default_class)
+print(decision_tree)

neural_network/nueralnet.py

@@ -0,0 +1,103 @@
+#imports
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.nn.functional as F
+from torch.utils.data import DataLoader
+import torchvision.datasets as datasets
+import torchvision.transforms as transforms
+
+#create fully connected network
+class NN(nn.Module):
+    def __init__(self, input_size, num_classes): #1 layer (28x28 = 784 nodes)
+        super(NN,self)._init_()
+        self.fc1 = nn.Linear(input_size, 50)
+        self.fc2 = nn.Linear(50,num_classes)
+
+    def forward(self, x):
+        x = F.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x
+
+# model = NN(784, 10)
+# x = torch.rand(64, 784)
+# print(model(x).shape)
+
+#set device
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+#hyperparameters
+input_size = 784
+num_classes = 10
+learning_rate = 0.001
+batch_size = 64
+num_epochs = 1
+
+#load data
+train_dataset = datasets.MNIST(root='dataset/', train = True, transform = transforms.toTensor(), download = True)
+train_loader = DataLoader(dataset= train_dataset, batch_size = batch_size, shuffle = True)
+test_dataset = datasets.MNIST(root='dataset/', train = False, transform = transforms.toTensor(), download = True)
+test_loader = DataLoader(dataset= test_dataset, batch_size = batch_size, shuffle = True)
+
+
+#initialize network 
+model = NN(input_size=input_size, num_classes=num_classes).to(device)
+
+#loss and optimizer
+criterion = nn.CrossEntropyLoss()
+optimizer = optim.Adam(model.parameters(), lr=learning_rate)
+
+#train network
+for epoch in range(num_epochs):
+    for batch_idx, (data, targets) in enumerate(train_loader):
+        #get data to cuda if possible
+        data = data.to(device = device)
+        targets = targets.to(device = device)
+
+        #get to correct shape
+        data = data.reshape(data.shape[0], -1)
+
+        # forward
+        scores = model(data)
+        loss = criterion(scores, targets)
+
+        #backward
+        optimizer.zero_grad()
+        loss.backward()
+
+        #gradient descent or adam step
+        optimizer.step()
+
+
+#check accuracy on training and test to see how good our model
+
+def check_accuracy(loader, model):
+    if loader.dataset.train: 
+        print("Checking accuracy on training data")
+    else:
+        print("Checking accuracy on test data")
+
+    num_correct = 0
+    num_samples = 0
+    model.eval()
+
+    with torch.no_grad():
+        for x,y in loader:
+            x = x.to(device = device)
+            y = y.to(device = device)
+            x = x.reshape(x.shape[0], -1)
+
+            scores = model(x)
+            _, predictions = scores.max(1)
+            num_correct += (predictions == y).sum()
+            num_samples +=  predictions.size(0)
+
+        print(f'Got {num_correct} / {num_samples} with accuracy {float(num_correct)/float(num_samples)*100:.2f}')
+             
+    model.train()
+    return acc
+  
+check_accuracy(train_loader, model) 
+check_accuracy(test_loader, model)
+
+