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1527_159_252_1434_144.py

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+# -*- coding: utf-8 -*-
+"""1527.159.252.1434.144
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1wIIHKVp7xmSZhl44znoh45yNm8bZPAYi
+"""
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import numpy as np
+
+class DirectionManipulator:
+  def __init__(self, latent_dim, target_direction):
+    self.latent_dim = latent_dim
+    self.target_direction = target_direction / torch.norm(target_direction)
+
+    self.transform = nn.Parameter(torch.eye(latent_dim))
+    self.optimizer = optim.Adam([self.transform], lr=0.001)
+
+  def get_direction(self, vectors):
+    centered = vectors = vectors.mean(dim=0, keepdim=True)
+
+    U, S, V = torch.svd(centered)
+
+    return V[:, 0]
+
+    def transform_vectors(self, vectors):
+      return torch.matmul(vectors, self.transform)
+
+    def compute_loss(self, vectors):
+      transformed = self.transform_vectors(vectors)
+      current_directoin = self.get_direction(transformed)
+
+      alignment_loss = -torch.abs(torch.dot(current_direction, self.target_direction))
+
+      identity = torch.eye(self.latent_dim, device=vectors.device)
+      orthogonality_loss = torch.norm(torch.matmul(self.transform, self.transform.t()) - identity)
+      return alignment_loss + 0.1 * orthogonality_loss
+
+    def train_step(self, vectors):
+      self.optimizer.zero_grad()
+      loss = self.compute_loss(vectors)
+      loss.backward()
+      self.optimizer.step()
+      return loss.item()
+
+    def fit(self, vectors, n_epochs=100):
+      losses = []
+      for epoch in range(n_epochs):
+        loss = self.train_step(vectors)
+        losses.append(loss)
+      return losses
+
+    def main():
+      latent_dim = 8
+      n_samples = 100
+
+      vectors = torch.randn(n_samples, latent_dim)
+      target_direction = torch.randn(latent_dim)
+
+      manipulator = DirectionManipulator(latent_dim, target_direction)
+      losses = manipulator.fit(vectors)
+
+      new_vectors = torch.randn(10, latent_dim)
+      transformed = manipulator.transform_vectors(new_vectors)
+
+      return transformed
+
+    if __name__ == "__main__":
+      main()
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+
+class DirectionManipulator:
+  def __init__(self, latent_dim, target_direction):
+    self.latent_dim = latent_dim
+    self.target_direction = target_direction / torch.norm(target_direction)
+    self.transform = nn.Parameter(torch.eye(latent_dim))
+    self.optimizer = optim.Adam([self.transform], lr=0.001)
+
+  def get_direction(self, vectors):
+    centered = vectors - vectors.mean(dim=0, keepdim=True)
+    U, S, V = torch.svd(centered)
+    return V[:, 0]
+
+  def transform_vectors(self, vectors):
+    return torch.matmul(vectors, self.transform)
+
+  def compute_loss(self, vectors):
+    transformed = self.transform_vectors(vectors)
+    current_direction = self.get_direction(transformed)
+    alignment_loss = -torch.abs(torch.dot(current_direction, self.target_direction))
+
+    identity = torch.eye(self.latent_dim, device=vectors.device)
+    orthogonality_loss = torch.norm(torch.matmul(self.transform, self.transform.t()) - identity)
+    return alignment_loss + 0.1 * orthogonality_loss
+
+  def train_step(self, vectors):
+    self.optimizer.zero_grad()
+    loss =self.compute_loss(vectors)
+    loss.backward()
+    self.optimizer.step()
+    return loss.item()
+
+  def fit(self, vectors, n_epochs=100):
+    losses = []
+    for epoch in range(n_epochs):
+      loss = self.train_step(vectors)
+      losses.append(loss)
+    return losses
+
+def visualize_transformation(original_vectors, transformed_vectors, original_direction, target_direction, title):
+  fig = plt.figure(figsize=(15, 5))
+
+  ax1 = fig.add_subplot(121, projection='3d')
+  ax1.scatter(original_vectors[:, 0],
+              original_vectors[:, 1],
+              original_vectors[:, 2],
+              c='blue', alpha=0.6, label='Original points')
+
+  ax1.quiver(0, 0, 0,
+             original_direction[0].item(),
+             original_direction[1].item(),
+             original_direction[2].item(),
+             color='red', linewidth=3, label='Original direction'
+             )
+
+  ax1.set_title('Original Data')
+  ax1.set_xlabel('X')
+  ax1.set_ylabel('Y')
+  ax1.set_zlabel('Z')
+  ax1.legend()
+
+  ax2 = fig.add_subplot(122, projection='3d')
+  ax2.scatter(transformed_vectors[:, 0],
+              transformed_vectors[:, 1],
+              transformed_vectors[:, 2],
+              c='green', alpha=0.6, label='Transformed points')
+
+  ax2.quiver(0, 0, 0,
+             target_direction[0].item(),
+             target_direction[1].item(),
+             target_direction[2].item(),
+             color='red', linewidth=3, label='Target direction')
+
+  ax2.set_title('Transformed Data')
+  ax2.set_xlabel('X')
+  ax2.set_ylabel('Y')
+  ax2.set_zlabel('Z')
+  ax2.legend()
+
+  plt.suptitle(title)
+  plt.tight_layout()
+  return fig
+
+def main():
+  torch.manual_seed(42)
+  latent_dim = 3
+  n_samples =100
+
+  direction = torch.tensor([1.0, 0.2, 0.1])
+  noise = torch.randn(n_samples, latent_dim) * 0.3
+  vectors = direction.repeat(n_samples, 1) + noise
+
+  target_direction = torch.tensor([0.2, 1.0, 0.1])
+
+  manipulator = DirectionManipulator(latent_dim, target_direction)
+  losses = manipulator.fit(vectors, n_epochs=200)
+
+  transformed_vectors = manipulator.transform_vectors(vectors)
+
+  original_direction = manipulator.get_direction(vectors)
+
+  fig = visualize_transformation(
+      vectors.detach().numpy(),
+      transformed_vectors.detach().numpy(),
+      original_direction.detach(),
+      target_direction,
+      title="Direction Manipulation Visualization"
+  )
+
+  plt.figure(figsize=(10, 4))
+  plt.plot(losses)
+  plt.title('Training Loss')
+  plt.xlabel('Epoch')
+  plt.ylabel('Loss')
+  plt.grid(True)
+  plt.show()
+
+  return vectors, transformed_vectors, losses
+
+if __name__ == "__main__":
+  main()
+