In Progress

Author: i338425

interaction_network.py

@@ -9,7 +9,7 @@
 
 import numpy as np
 import time
-from physics_engine import gen
+from physics_engine import gen, make_video
 FLAGS = None
 
 def m(O,Rr,Rs,Ra):
@@ -108,11 +108,12 @@ def train():
   tf.global_variables_initializer().run();
 
   # Data Generation
-  set_num=2000;
+  #set_num=2000;
+  set_num=1;
   total_data=np.zeros((999*set_num,FLAGS.Ds,FLAGS.No),dtype=object);
   total_label=np.zeros((999*set_num,FLAGS.Dp,FLAGS.No),dtype=object);
   for i in range(set_num):
-    raw_data=gen(FLAGS.No,False);
+    raw_data=gen(FLAGS.No,True);
     data=np.zeros((999,FLAGS.Ds,FLAGS.No),dtype=object);
     label=np.zeros((999,FLAGS.Dp,FLAGS.No),dtype=object);
     for j in range(1000-1):
@@ -121,8 +122,10 @@ def train():
     total_label[i*999:(i+1)*999,:]=label;
 
   # Shuffle
-  tr_data_num=1000000;
-  val_data_num=200000;
+  #tr_data_num=1000000;
+  #val_data_num=200000;
+  tr_data_num=400;
+  val_data_num=300;
   total_idx=range(len(total_data));np.random.shuffle(total_idx);
   mixed_data=total_data[total_idx];
   mixed_label=total_label[total_idx];
@@ -149,7 +152,8 @@ def train():
         cnt+=1;
 
   # Training
-  for i in range(2000):
+  max_epoches=2000;
+  for i in range(max_epoches):
     for j in range(int(len(train_data)/mini_batch_num)):
       batch_data=train_data[j*mini_batch_num:(j+1)*mini_batch_num];
       batch_label=train_label[j*mini_batch_num:(j+1)*mini_batch_num];
@@ -160,6 +164,23 @@ def train():
       batch_label=val_label[j*mini_batch_num:(j+1)*mini_batch_num];
       val_loss+=sess.run(loss,feed_dict={O:batch_data,Rr:Rr_data,Rs:Rs_data,Ra:Ra_data,P_label:batch_label,X:X_data});
     print("Epoch "+str(i+1)+" Validation MSE: "+str(val_loss/(j+1)));
+
+  # Make Video
+  frame_len=250;
+  raw_data=gen(FLAGS.No,True);
+  xy_origin=raw_data[:frame_len,:,1:3];
+  estimated_data=np.zeros((frame_len,FLAGS.No,FLAGS.Ds),dtype=float);
+  estimated_data[0]=raw_data[0];
+  for i in range(1,frame_len):
+    velocities=sess.run(P,feed_dict={O:[np.transpose(estimated_data[i-1])],Rr:[Rr_data[0]],Rs:[Rs_data[0]],Ra:[Ra_data[0]],X:[X_data[0]]})[0];
+    estimated_data[i,:,0]=estimated_data[i-1][:,0];
+    estimated_data[i,:,3:5]=np.transpose(velocities);
+    estimated_data[i,:,1:3]=estimated_data[i-1,:,1:3]+estimated_data[i,:,3:5]*0.001;
+  xy_estimated=estimated_data[:,:,1:3];
+  print("Video Recording");
+  make_video(xy_origin,"true.mp4");
+  make_video(xy_estimated,"modeling.mp4");
+  
 
 def main(_):
   FLAGS.log_dir+=str(int(time.time()));

modeling.mp4

@@ -19,14 +19,27 @@
 # 5 features on the state [mass,x,y,x_vel,y_vel]
 fea_num=5;
 # G 
-G = 6.67428e-11;
+#G = 6.67428e-11;
+G=10**5;
 # time step
 diff_t=0.001;
 
 def init(total_state,n_body,fea_num,orbit):
   data=np.zeros((total_state,n_body,fea_num),dtype=float);
   if(orbit):
-    print("Not yet");exit(1);
+    data[0][0][0]=100;
+    data[0][0][1:5]=0.0;
+    for i in range(1,n_body):
+      data[0][i][0]=np.random.rand()*8.98+0.02;
+      distance=np.random.rand()*90.0+10.0;
+      theta=np.random.rand()*360;
+      theta_rad = pi/2 - radians(theta);    
+      data[0][i][1]=distance*cos(theta_rad);
+      data[0][i][2]=distance*sin(theta_rad);
+      data[0][i][3]=-1*data[0][i][2]/norm(data[0][i][1:3])*(G*data[0][0][0]/distance**2)*0.05;
+      data[0][i][4]=data[0][i][1]/norm(data[0][i][1:3])*(G*data[0][0][0]/distance**2)*0.05;
+      #data[0][i][3]=np.random.rand()*10.0-5.0;
+      #data[0][i][4]=np.random.rand()*10.0-5.0;
   else:
     for i in range(n_body):
       data[0][i][0]=np.random.rand()*8.98+0.02;
@@ -69,26 +82,27 @@ def gen(n_body,orbit):
   data=init(total_state,n_body,fea_num,orbit);
   for i in range(1,total_state):
     data[i]=calc(data[i-1],n_body);
+    print(norm(data[i][0,1:3]-data[i][1,1:3]));
   return data;
 
-def make_video(xy):
+def make_video(xy,filename):
   os.system("rm -rf pics/*");
   FFMpegWriter = manimation.writers['ffmpeg']
   metadata = dict(title='Movie Test', artist='Matplotlib',
                   comment='Movie support!')
   writer = FFMpegWriter(fps=15, metadata=metadata)
   fig = plt.figure()
-  plt.xlim(-1000, 1000)
-  plt.ylim(-1000, 1000)
+  plt.xlim(-200, 200)
+  plt.ylim(-200, 200)
   fig_num=len(xy);
   color=['ro','bo','go','ko','yo','mo','co'];
-  with writer.saving(fig, "video.mp4", len(xy)):
+  with writer.saving(fig, filename, len(xy)):
     for i in range(len(xy)):
       for j in range(len(xy[0])):
         plt.plot(xy[i,j,1],xy[i,j,0],color[j%len(color)]);
       writer.grab_frame();
 
 if __name__=='__main__':
-  data=gen(6,False);
-  #xy=data[:,:,1:3];
-  #make_video(xy);
+  data=gen(6,True);
+  xy=data[:,:,1:3];
+  make_video(xy,"test.mp4");