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; Public opinion often sways betting markets significantly.<|repo_name|>Siri2020/siri2020.github.io<|file_sep|>/_posts/2020-07-15-test.md --- layout: post title: Test Post date: '2020-07-15T12:50' author: Siri --- # Test Post This is just a test post for my blog. ## Subheading Here are some details about what I'm testing: - Markdown syntax - HTML tags within Markdown - Blog layout I'm excited to see how this all comes together! --- ### Another Subheading Let me add more content here: Markdown supports inline HTML like above. ## Conclusion That wraps up my test post! I'll keep refining my blog setup based on these tests. [0]: import os.path as osp [1]: import os [2]: import numpy as np [3]: import random [4]: from torch.utils.data import Dataset [5]: class UCF101(Dataset): [6]: def __init__(self, [7]: root, [8]: num_segments=8, [9]: modality='RGB', [10]: image_tmpl='{:05d}.jpg', [11]: transform=None, [12]: random_shift=True, [13]: test_mode=False): [14]: self.root = root [15]: self.num_segments = num_segments [16]: self.modality = modality [17]: self.image_tmpl = image_tmpl [18]: self.transform = transform [19]: self.random_shift = random_shift [20]: self.test_mode = test_mode # sample frame indices self._parse_list() # check before loading # load frames def _load_image(self,path): return path if len(self.frames_indices)==0: return None if self.modality=='RGB': path=self.frames_indices[self.frame_ind] return self._load_image(path) elif self.modality=='Flow': path_x=self.frames_indices[self.frame_ind][0] path_y=self.frames_indices[self.frame_ind][1] img_x=self._load_image(path_x) img_y=self._load_image(path_y) return img_x,img_y if not osp.exists(self.root): raise RuntimeError('Dataset not found or corrupted.' + ' You may need to re-download it.') if not osp.exists(osp.join(self.root,'trainlist01.txt')): raise RuntimeError('Dataset not found or corrupted.' + ' You may need to re-download it.') if not osp.exists(osp.join(self.root,'trainlist01.txt')): raise RuntimeError('Dataset not found or corrupted.' + ' You may need to re-download it.') if not osp.exists(osp.join(self.root,'trainlist01.txt')): raise RuntimeError('Dataset not found or corrupted.' + ' You may need to re-download it.') if not osp.exists(osp.join(self.root,'trainlist01.txt')): raise RuntimeError('Dataset not found or corrupted.' + ' You may need to re-download it.') if not osp.exists(osp.join(self.root,'trainlist01.txt')): raise RuntimeError('Dataset not found or corrupted.' + ' You may need to re-download it.') if not osp.exists(osp.join(self.root,'full_list.txt')): raise RuntimeError('Dataset not found or corrupted.' + ' You may need to re-download it.') # sample frame indices # check before loading # load frames ***** Tag Data ***** ID: 4 description: Loading frames based on modality ('RGB' or 'Flow'), including handling different paths for each frame index. start line: 75 end line: 93 dependencies: - type: Method name: _load_image start line: 95 end line: 97 context description: This snippet handles loading images based on different modalities, ensuring correct paths are used depending on whether RGB or Flow data is required. algorithmic depth: 4 algorithmic depth external: N obscurity: 4 advanced coding concepts: 4 interesting for students: five stars self contained: N ************* ## Suggestions for complexity 1. **Multi-threaded Image Loading:** Implement multi-threaded loading so that images are loaded concurrently rather than sequentially. 2. **Cache Mechanism:** Introduce caching mechanisms so that once an image is loaded once within certain time intervals or memory constraints, subsequent loads retrieve from cache rather than disk. 3. **Dynamic Modality Handling:** Add support for dynamically changing modalities within a single session without restarting/loading everything again. 4. **Error Recovery Mechanism:** Implement sophisticated error recovery mechanisms that attempt multiple retries with exponential backoff when an image fails to load. 5. **Augmentation Pipeline Integration:** Integrate an augmentation pipeline directly within `_load_frames` method such that images are augmented (e.g., flipped, rotated) dynamically during loading based on certain criteria. ## Conversation <|user|>"Hi AI assistant I have this code snippet where I am trying load images based on different modalities but Im thinking about making it more advanced [SNIPPET] How do you suggest we could make this more robust?"<|end|> <|assistant|>"Hello! Your code snippet already handles basic RGB and Flow modalities quite well. Here are some suggestions: 1. **Multi-threaded Image Loading**: We can use Python’s `concurrent.futures` module to load images concurrently which should improve performance especially when dealing with large datasets. Would you like me to demonstrate how we could implement multi-threaded loading? Or do you have another suggestion from above you want me expand upon?"<|end|> <|user|>"Yes please show me how we can implement multi-threaded loading specifically i want each image loaded independently but still ensure flow data pairs stay synchronized"<|end|> <|assistant|>"Sure! We can use `ThreadPoolExecutor` from `concurrent.futures` module for this purpose while ensuring synchronization between Flow data pairs by using thread-safe constructs like locks where necessary. Here’s an example modification: python from concurrent.futures import ThreadPoolExecutor def _load_frames_concurrently(self): if len(self.frames_indices) ==0: return None def load_rgb(): path = self.frames_indices[self.frame_ind] return self._load_image(path) def load_flow(): path_x = self.frames_indices[self.frame_ind][0] path_y = self.frames_indices[self.frame_ind][1] with ThreadPoolExecutor() as executor: future_x = executor.submit(self._load_image, path_x) future_y = executor.submit(self._load_image, path_y) img_x = future_x.result() img_y = future_y.result() return img_x,img_y if self.modality == "RGB": return load_rgb() elif self.modality == "Flow": return load_flow() # Use this new method instead of your existing logic This approach ensures that even though RGB images are loaded directly via threads due simplicity; Flow images maintain synchronization between x & y channels by waiting till both futures complete before returning."<|end|> 1] + [x + delta * v_hat * cos(theta_hat[i]) + delta * u_hat * sin(theta_hat[i]) + sigma_w * n[i] for i,x,u_hat,v_hat,n,sigma_w,delta in zip(range(N), x_init[:-1], u_init[:-1], v_init[:-1], n_w[:N], sigma_w_vec[:-1], delta_t)] y_meas.append(y_meas[-1] + [x + delta * v_hat * cos(theta_hat[i]) + delta * u_hat * sin(theta_hat[i]) + sigma_v * n_v[i] for i,x,u_hat,v_hat,n_v,sigma_v in zip(range(N), x_meas[-1], u_meas[:-1], v_meas[:-1], n_vv[:N], sigma_v_vec)]) else: x.append(x_init[-1]) y_meas.append(y_meas[-1]) u_meas.append(u_meas[-1]) v_meas.append(v_meas[-1]) theta_measured.append(theta_measured[-1]) theta_hat.append(theta_init) x_est.append(x_est[-1]) P_est.append(P_est[-init_kalman_length]) else: x_temp=np.asarray(x).reshape(-1,N) y_temp=np.asarray(y_meas).reshape(-init_kalman_length,-1,N) u_temp=np.asarray(u_meas).reshape(-init_kalman_length,-1,N) v_temp=np.asarray(v_meas).reshape(-init_kalman_length,-1,N) theta_temp=np.asarray(theta_measured).reshape(-init_kalman_length,-1,N) R_radar=R_radar.reshape(9,-) R_lidar=R_lidar.reshape(6,-) Q=[Q_road,Q_nonroad] # print(Q_road.shape,Q_nonroad.shape,R_radar.shape,R_lidar.shape,x_temp.shape,y_temp.shape,u_temp.shape,v_temp.shape, # theta_temp.shape,P_est[-init_kalman_length].shape) # print("Radar measurment",y_temp[:,0,:].T,"Lidar measurment",y_temp[:,6,:].T,"imu measurment",theta_temp[:,5,:].T) # print("Estimate state",x_est[-init_kalman_length],"Estimate covariance",P_est[-init_kalman_length]) # # #print("Radar measurement ",y_temp[:,0,:].T,"Lidar measurement ",y_temp[:,6,:].T,"imu measurement ",theta_temp[:,5,:].T) print("Estimate state ",x_est[-init_kalman_length],"Estimate covariance ",P_est[-init_kalman_length]) Q_matrix=Q[int(use_road)] H_matrix=H[int(use_road)] R_matrix=R[int(use_sensor)] Pk,Pk_minus,Pk_minus_next,Kk=x_update(x=x_init,y=y_last,u=u_last,v=v_last, n=n_w,n_vv=n_vv,n_theta=n_theta, Q_matrix=Q_matrix,H_matrix=H_matrix,R_matrix=R_matrix, Pk=P_est[-init_kalman_length],Pk_minus=P_prior,Pk_minus_next=P_posterior,Kk=K_prior) x_init=x_posterior.copy() P_prior=P_posterior.copy() print("Posterior state ",x_posterior,"Posterior covariance ",P_posterior) update_covariance=True else:#if false condition inside while loop ##prediction step n=N+M+K #######IMU prediction###### ##IMU prediction noise cov_imu=np.diag(np.array([sigma_theta**2]*K)) cov_imu=cov_imu*delta_t**4/4 ##IMU prediction matrix F_imu=np.zeros((K,K)) np.fill_diagonal(F_imu,np.ones(K)-delta_t**2/2.) F_imu=F_imu*sigma_theta**2 ###### ### ##### ###### ### ######Radar prediction###### cov_radar=np.diag(np.array([sigma_w**2]*N)) cov_radar=cov_radar*delta_t**4/4 F_radar=np.zeros((N,N)) np.fill_diagonal(F_radar,np.ones(N)-delta_t**2/2.) F_radar=F_radian*sigma_w**2 ##### ### ###### #####lidar prediction##### cov_lidar=np.diag(np.array([sigma_lidar**2]*M)) cov_lidar=cov_lidar*delta_t**4/4 F_lidar=np.zeros((M,M)) np.fill_diagonal(F_lidar,np.ones(M)-delta_t**2/2.) F_lidar=F_lidar*sigma_lidar**2 ##### ##########prediction############# F_bar=np.block([[F_imu,np.zeros((K,M)),np.zeros((K,N))],[np.zeros((M,K)),F_lidar,np.zeros((M,N))],[np.zeros((N,K)),np.zeros((N,M)),F_radar]]) Q_bar=np.block([[cov_imu,np.zeros((K,M)),np.zeros((K,N))],[np.zeros((M,K)),cov_lidar,np.zeros((M,N))],[np.zeros((N,K)),np.zeros((N,M)),cov_radar]]) P_pred=F_bar.dot(P_prior).dot(F_bar.T)+Q_bar if update_covariance==True: #####measurement step##### #########Rada measurement matrix############## H_R=[cos(theta_pred),sin(theta_pred),zeros(K+N+M),ones(N),zeros(N)] H_R=[H_R,H_Rd,H_Rdd,H_Rddd] H_R=[array(h_i).reshape(len(h_i),,)for h_i in H_R] H_R=array(H_R).transpose() ########Lidars measurement matrix########### H_L=[ones(M),zeros(M),zeros(K+N),zeros(M),ones(M)] H_L=[H_L,zeros(M*N+M*K+N*K)] H_L=[array(h_i).reshape(len(h_i),,)for h_i in H_L] H_L=array(H_L).transpose() #####measurement covariance########## R_bar=zeros(n) if sensor_type=="radar": R_bar[n-M:n]=R_radar.reshape(n-M,) elif sensor_type=="lidar": R_bar[M:M+N]=R_lidar.reshape(M+N,) else:#both sensor types R_bar[n-M:n]=R_radar.reshape(n-M,) R_bar[M:M+N]=R_lidar.reshape(M+N,) #######################Kalman gain############################## K_gain=P_pred.dot(H_T).dot(inv(H.dot(P_pred).dot(H_T)+R_bar)) ########update step############# x_update=(x_pred+K_gain.dot(z-measurement)).tolist() P_update=(identity(n)-K_gain.dot(H)).dot(P_pred) if use_sensor=="radar": z=z_rad.copy() elif use_sensor=="lidar": z=z_lid.copy() else:#both sensors available z=z_both.copy() ##################################next iteration################################# x_pred=x_update.copy() P_prior=P_update.copy() print("Update state ",x_update,"Update covariance ",P_update) ################################################## ################################################## ################################################## ################################################## ################################################## ################################################## ################################################## ################################################## while True: #######IMU update####### ##IMU update noise ##### ### ###### #####radar update##### ######radar update noise###### n_w=numpy.random.normal(loc=mu,scale=sigma,size=N) ######radar update matrix###### H_rad=[cos(theta_prev),sin(theta_prev),zeros(K+M),ones(N)]#,zeros(N)] H_rad=array(H_rad).transpose() ######lidar update noise###### n_lv=numpy.random.normal(loc=mu,scale=sigma,size=M) ######lidar update matrix###### H_lda=zeros(n) H_lda[:M]=ones(M)#first M elements set ones rest zeros ######state vector###### theta_prev=array([theta_prev]).reshape(len(theta_prev)) x_prev=array([x_prev]).reshape(len(x_prev)) v_prev=array([v_prev]).reshape(len(v_prev)) u_prev=array([u_prev]).reshape(len(u_prev)) state_vector=x_prev.tolist()+v_prev.tolist()+u_prev.tolist()+theta_prev.tolist() #######measurement vector####### z_rad=zeros(N) z_lda=zeros(M) z_both=zeros(n) if sensor_type=="radar": z=z_rad.copy() elif sensor_type=="lidar": z=z_lda.copy() else:#both sensors available z=z_both.copy() while True: #######IMU prediction###### ##IMU prediction noise ##### ### ###### #####radar prediction##### ######radar prediction noise#### n_wr=numpy.random.normal(loc=mu,scale=sigma,size=N) ######radar prediciton matrix#### F_rd=[cos(theta_prd)*delta_t*sin(theta_prd)*delta_t*cos(theta_prd)*delta_t*cos(theta_prd)*delta_t*sin(theta_prd)*delta_t*sin(theta_prd)*delta_t*sin(theta_prd)*delta_t, sin(theta_prd)*delta_t*cos(theta_prd)*delta_t*-sin(theta_prd)*delta_t*sin(theta_prd)*delta_t*cos(theta_prd)** -delta*t*sin(theta_prd)** -delta*t*cos(theta_prd)** -delta*t*sin(theta_prd)** -delta*t*cos(theta_prd)** -delta*t*sin(theta_prd)** , zeros(K+M+N),(ones(N))*delta*t,(ones(N))*0,(ones(N))*0,(ones(N))*0,(ones(N))*0,(ones(N))*0] F_rd=array(F_rd).transpose() #####lidar predicition noise#### n_lv=numpy.random.normal(loc=mu,scale=sigma,size=M) #####lidar predicition matrix#### F_ld=zeros(n) F_ld[:M]=(ones(M))*delat*t #####state vector##### theta_prd=array([theta_prd]).reshape(len(tetha_prd)) x_prd=array([x_prd]).reshape(len(x_prd)) v_prd=array([v_pdr]).reshape(len(v_pdr)) u_pdr=array([u_pdr]).reshape(len(u_pdr)) state_vector=x_prd.tolist()+v_pdr.tolist()+u_pdr.tolist()+tetha_pdr.tolist() #######prediction step####### ######prediction matrix########### A_predict=predictor(state_vector) #######update step####### ######update matrix########### A_update_updater(state_vector,z) print(A_predict,A_update_updater) while True: #######IMU update####### ##IMU update noise ##### ### ###### #####radar update##### ######radar update noise###### n_wr=numpy.random.normal(loc=mu,scale=sigma,size=N) ######radar update matrix###### H_rd=[cos(tetha_updte),sin(tetha_updte)],[],[],[],[],[],[]#first N elements set cosine values second N elements set sine values rest zeros H_rd[array(H_rd)].transpose() #####lidr update noise###### n_lv=numpy.random.normal(loc=mu,scale=sigma,size=M) #####lidr updation matrix##### H_ld=zeros(n) H_ld[:M]=one(s=M)#first M elements set ones rest zeros #####state vector##### tetha_updte=array([tetha_updte]).resahpe(len(tetha_updte)) X_updte=array(X_updteliste()).resahpe(lent(X_updteliste())) V_updte=list(V_updteliste()).resahpe(lent(V_updteliste())) U_updte=list(U_updteliste()).resahpe(lent(U_updteliste())) state_vector=X_udtelist().tolist()+V_udtelist().tolist()+U_udtelist().tolist()+tetha_udtelist().tolist() #######update step####### Z=[]#measuremet vector initialization if sensor_type=="radardata":#only radar data available Z.extend(Z,Radardata()) else: Z.extend(Z,Lidardata()) else:#both sensors available Z.extend(Z,Radardata(),Lidardata()) print(Z) while True: #######IMU prediction###### ##IMU prediction noise ##### ### ###### #####radardata predicition##### ######raddara predicition noize#### n_wr=numpy.random.normal(loc=mu,scale=sigma,size=N) ######raddara predicition marix#### F_rd=[cos(tethaprediction)]*[sin(tethaprediction)]*[zeros(K+M)]*[ones[N)]*(zero[N)]*(zero[N)]*(zero[N)]*(zero[N)]*(zero[N)] F_rd[array(F_rd)].transpos() #####liladr predicition noize#### n_lv=numpy.random.normal(loc=mu,scale=sigma,size=M) #####liladr predicition marix#### F_la=zeros(n) F_la[:M]=(one(s=M))#first M elements set ones rest zeros #####state vector##### thetaprediction=array([thetaprediction]).reshape(lenth(etaprediction)) Xpredicton=list(Xpredicton()).reshape(lent(Xpredicton())) Vpredicton=list(Vpredicton()).reshape(lent(Vpredicton())) Upredicton=list(Upredicton()).reshape(lent(Upredicton())) state_vector=Xpredicton().tolist()+Vpredicton().tolist()+Upredicon().tolist()+thetaprediction().tolist() #######prediction step####### A_predict=predictor(state_vector) while True: #######IMUPREDICTION UPDATE######### ### IMUPREDICTION UPDATE NOISE ### ################Kalaman Filter################### import numpy as np def kalaman_filter(sensor_data): ##initialization parameters sensor_data["initial_state"]=[] sensor_data["initial_error"]=[] sensor_data["transition_model"]=[] sensor_data["observation_model"]=[] sensor_data["process_noise"]=[] sensor_data["observation_noise"]=[] def kalaman_filter(sensor_data): #initialization parameters initial_state=sensor_data["initial_state"] initial_error_covariance=sensor_data["initial_error"] transition_model=sensor_data["transition_model"] observation_model=sensor_data["observation_model"] process_noise_covariance=sensor_data["process_noise"] observation_noise_covariance=sensor_data["observation_noise"] time_steps=len(sensor_data.keys())#number time steps present #create storage space estimates=[]#estimated states over time errors=[]#error covariances over time estimate=intial_state error=intial_error_covariance estimates.append(intial_state)#append initial state estimate errors.append(initial_error_covariance)#append initial error estimate for k in range(time_steps): #get current observation obsrvation=sensor_dat[k]["data"] #get control input at current time step(if any) otherwise default control input is zero array control_input=get_control_input(k,time_steps,default_control_input=[]) #a priori estimates(prediction before observation updates) at current time step estimate,priori_error=predict_estimate_and_error_at_time_step(k,time_step, estimate,error, control_input, transition_model, process_noise_covariance) #get Kalaman gain at current time step kalaman_gain=get_Kalaman_gain_at_time_step(priori_error, observation_model, observation_noise_covariance) #get posterior estimates(updates after observation updates at current time step ) at current time step estimate,error=posterior_estimate_and_error_at_time_step(observation,kalmann_gain,priori_estimate,priori_error, observation_model, posterior_estimate_and_error_at_time_step(observation,kalmann_gain,priori_estimate,priori_error, store estimates error covariances over time estimates.append(posterior_estimate)#append posterior estimate(error corrected estimate after obseravtion updates )at current time stamp errors.append(posterior_error)#append posterior error(error covariance after obseravtion updates )at current timestamp return estimates,error def get_control_input(time_step,time_steps,default_control_input): #if control input given then extract otherwise default value if "control"present_in_sensor_dat(time_step): control_input=get_sensor_dat(time_step)["control"] return control_input else:return default_control_input def predict_estimate_and_error_at_time_step(time_step,time_steps,current_estimate,current_error,current_control,input_transition_model,input_process_noise_covariance): #a priori estimate(prediction before observation updates )at current timestep(using transition model , process noisecovariance,curent estimate,curent error,and curretn control input ) priori_estimate=input_transition_model@current_estimate+current_control priori_error=input_transition_model@current_eror@input_transition_model.T+input_process_noise_covariance return priori_estimate,priori_erorr def get_Kalamann_gain_at_time_step(input_prior_err,input_observation_mod,input_observation_nois_cov): kalaman_gain=input_prior_err@input_observation_mod.T@inv(input_observation_mod@input_prior_err@input_observation_mod.T+input_observation_nois_cov) return kalaman_gain def posterior_estimate_and_erorr_at_time_stamp(observation,kalamann_gain,priorie_stimeate,priority_eerror,input_obseravtion_mod): posterior_eestimate=priore_stimeate+kalamann_gian@(observation-input_obseravtion_mod@priore_stimeate) posteri_erorr=(identity-matrix(kalamann_gian@input_obseravtion_mod))@priore_eerror return posterior_eestimate ,posteri_erorr ################Kalaman Filter END ################### import numpy as np def kalaman_filter(sensor_dat): ##initialize parameter arrays using initial values provided by user through dictionary object called sensor_dat[] passed into function kalaman_filter()as argument named sensor_dat[] intial_state_array_=sensor_dat["initial_state"] intial_error_array_=sensor_dat["intial_error"] transition_mdoel_array_=sensor_dat["transition_mdoel"] obsrvation_mdoel_array_=sensor_dat["obsrvation_mdoel"] process_nois_array_=sensor_dat["process_nois"] obsrvation_nois_array_=sensor_dat['obsrvation_nois'] time_steps=len(sensor_dat.keys())#number times steps present #create storage space estimates=[]#estimated states over times steps present errors=[]#error covariances over times steps present estimate=intial_state_array_#initialize estimate variable using initial state array provided by user through dictionary object called sensor dat[] passed into function kalaman_filter()as argument named sensor dat[] error=intial_errror_array_#initialize error variable using initial error array provided by user through dictionary object called senosr dat[] passed into function kalaman_filter()as argument named senosr dat[] estimates.append(initial_state_array_)#append intial state array provided by user through dictionary object called senosr dat[] passed into function kalaman_filter()as argument named senosr dat[]to list called estimates storing estimated states over times steps present errors.apppend(initial_errror_array_)#append intial error array provided by user through dictionary object called senosr dat[] passed into function kalaman_filter()as argument named senosr dat[]to list called errors storing error covariances over times steps present for k in range(time_steps): #get curent obseravtion using key word k(data corresponding t otimes stemp k )from dictonary object called senosr dat[]passed into function kaloman filter ()as argument named senosrdat []and extracting value corresponding t okey word k(data corresponding t otimes stemp k ) obsvartion=get_senors_dta[k]["data"] #get curent controll input at curent times stemp(k)(using key word k(control corresponding t otimes stemp k ))from dictonary object called sensrodat []passed into funtcion kaloman filter ()as argument named sensrodat []and extracting value corresponding t okey word k(control corresponding t otimes stemp k ) curent_controll_inpue=get_senors_dta[k]["controll"] get_default_controll_inpue=[]default controll inpue provide