From c75431ba442cc0673549a186f5685bd30cef3756 Mon Sep 17 00:00:00 2001
From: orbxball <orbxball@protonmail.com>
Date: Sat, 13 May 2017 05:22:18 +0800
Subject: [PATCH] hand rotation in problem 3

---
 hw4/hand_estimation.py | 134 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 134 insertions(+)
 create mode 100644 hw4/hand_estimation.py

diff --git a/hw4/hand_estimation.py b/hw4/hand_estimation.py
new file mode 100644
index 0000000..ce1884b
--- /dev/null
+++ b/hw4/hand_estimation.py
@@ -0,0 +1,134 @@
+import sys, os
+import argparse
+import numpy as np
+import gendata
+from sklearn.neighbors import NearestNeighbors
+from scipy import misc
+
+
+def get_param():
+  N = np.random.randint(1, 11) * 10000
+  # the hidden dimension is randomly chosen from [60, 79] uniformly
+  layer_dims = [np.random.randint(60, 80), 100]
+  return N, layer_dims
+
+
+def sampling(N, X):
+  permu = np.random.permutation(N)[:sample_size]
+  return X[permu,:]
+
+
+def NN(fitting_data, query_data):
+  nbrs = NearestNeighbors(n_neighbors=2, algorithm='ball_tree', n_jobs=-1)
+  return nbrs.fit(fitting_data).kneighbors(query_data)
+
+
+def get_index(target, nparray):
+  array = list(nparray)
+  idx = 0
+  for i in range(len(array)-1):
+    if array[i] < target and target <= array[i+1]:
+      idx = (target - array[i]) / (array[i+1] - array[i]) + i
+      break
+  if target >= array[len(array)-1]:
+    idx = len(array)-1
+  return idx
+
+
+def train(sample_size, round_size, table_name):
+  for dim in range(1, 61):
+    average_distance = 0.0
+    print('Dim: {}'.format(dim))
+    for r in range(round_size):
+      N, layer_dims = get_param()
+      X = gendata.gen_data(dim, layer_dims, N)
+
+      sample_X = sampling(N, X)
+      distances, indices = NN(X, sample_X)
+
+      avg_d = np.mean(distances[:,1])
+      print('Round {}: Start NN with N={} hidden_dim={}, distance => {}'.format(r, N, layer_dims[0], avg_d))
+      average_distance += avg_d
+    average_distance /= round_size
+    print('average distance => {}'.format(average_distance))
+    table.append(average_distance)
+
+  print('Saving table...')
+  np.save(table_name, np.array(table))
+  print('Saved')
+  return table
+
+
+def validate(model, sample_size):
+  error = 0.0
+  test_size = 200
+  validate_round_size = 10
+  for i in range(test_size):
+    total_avg = 0.0
+    dim = np.random.randint(1, 61)
+    N, layer_dims = get_param()
+    X = gendata.gen_data(dim, layer_dims, N)
+
+    for j in range(validate_round_size):
+      sample_X = sampling(N, X)
+      distances, indices = NN(X, sample_X)
+
+      avg_d = np.mean(distances[:,1])
+      total_avg += avg_d
+    predicted = get_index(total_avg/validate_round_size, model) + 1
+    print('Validate {}: true => {} predicted => {}'.format(i, dim, predicted))
+    error += np.absolute(np.log(predicted) - np.log(dim))
+  error /= test_size
+  print('Validation: {}'.format(error))
+
+
+def main():
+  try:
+    print('Loading model...')
+    model = np.load(model_name)
+    print('Loading Done!')
+  except:
+    print('Start training...')
+    table = train(sample_size, round_size, table_name)
+    print('Training Done!')
+    model = np.array(table)
+
+  # validate(model, sample_size)
+  # sys.exit(-1)
+
+  if hand_dir != None:
+    img_matrix = []
+    for img in os.listdir(hand_dir):
+      pic = misc.imread(os.path.join(hand_dir, img))
+      new_pic = misc.imresize(pic, (10, 10))
+      img_matrix.append(new_pic.flatten())
+    img_matrix = np.array(img_matrix)
+    std = np.std(img_matrix, axis=0)
+    mean = np.mean(img_matrix, axis=0)
+    img_matrix = (img_matrix - mean) / std
+    sample_img_matrix = sampling(img_matrix.shape[0], img_matrix)
+
+    distances, indices = NN(img_matrix, sample_img_matrix)
+    avg_d = np.mean(distances[:,1])
+    predicted = get_index(avg_d, model) + 1
+    print(predicted)
+
+
+if __name__ == '__main__':
+  parser = argparse.ArgumentParser(description='problem 3: Estimation of Intrinsic Diemnsion')
+  parser.add_argument('--data', metavar='<#datapath>', type=str)
+  parser.add_argument('--hand', metavar='<#hand path>', type=str, required=True)
+  args = parser.parse_args()
+
+  data_path = args.data
+  hand_dir = args.hand
+
+  # table record the average distance of dimension
+  table = []
+  model_dir = './model'
+  table_name = os.path.join(model_dir, 'model2.npy')
+  sample_size = 50
+  round_size = 50
+  model_name = os.path.join(model_dir, 'model2.npy')
+
+  main()