EURO U19 Qualification Group 9 stats & predictions
Overview of the Football EURO U19 Qualification Group 9
The Football EURO U19 Qualification Group 9 is set to witness an exciting series of matches tomorrow, where young talents from various nations will compete for a spot in the prestigious tournament. This group comprises some of the most promising young footballers in Europe, making every match a thrilling spectacle. Fans and bettors alike are eagerly anticipating the outcomes, with expert predictions already circulating.
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Each team brings its unique strengths and strategies to the field, creating a dynamic and unpredictable competition. The matches are not only crucial for qualification but also serve as a platform for these young athletes to showcase their skills on an international stage.
Match Schedule and Key Highlights
The day’s schedule includes several key matches that could determine the fate of teams vying for qualification. Here’s a breakdown of what to expect:
- Match 1: Team A vs Team B
- Team A has been in excellent form recently, boasting a strong defense and a potent attack led by their star forward.
- Team B, known for their tactical play and midfield dominance, will look to exploit any weaknesses in Team A’s lineup.
- Match 2: Team C vs Team D
- Team C enters this match with high confidence after a series of impressive victories. Their young goalkeeper has been particularly outstanding.
- Team D, despite recent setbacks, remains dangerous due to their fast-paced wingers who can change the game in seconds.
- Match 3: Team E vs Team F
- This match is expected to be highly competitive, with both teams having evenly matched squads. It will likely come down to individual brilliance on the day.
- Spectators can look forward to a tactical battle as both managers are known for their strategic acumen.
Betting Predictions and Expert Analysis
Betting enthusiasts have been closely analyzing statistics and trends leading up to these matches. Here are some expert predictions:
- Prediction for Match 1: Team A vs Team B
- Prediction for Match 2: Team C vs Team D
- Prediction for Match 3: Team E vs Team F
Analyzing recent performances, experts predict a narrow victory for Team A. Their defensive solidity coupled with clinical finishing makes them favorites. Betting odds suggest backing Team A to win by a one-goal margin.
The prediction here leans towards a draw due to the evenly matched nature of both teams. However, betting tips favoring over two goals being scored seem promising given both teams' attacking prowess.
This match is anticipated to be tight, with many predicting another draw. Yet, there's potential value in betting on underdogs if you're feeling adventurous.
In-Depth Player Analysis
Focusing on individual performances can provide deeper insights into how these matches might unfold:
- Star Player from Team A:
- Midfield Maestro from Team B: .5:
img = np.rot90(img)
if np.random.rand() > .5:
img = np.flip(img)
augmented_data.append(img)
return torch.stack(augmented_data)
# Custom learning rate scheduler logic.
def adjust_learning_rate(optimizer):
if self.no_improve_epochs >= patience_threshold:
new_lr = optimizer.param_groups[0]['lr'] * decay_factor
optimizer.param_groups[0]['lr'] = new_lr
patience_threshold = 3
decay_factor = .5
# Training loop with detailed logging.
num_epochs = config['num_epochs']
log_interval = config['log_interval']
for epoch in range(num_epochs):
self.model.train()
running_loss = RunningAverageMeter()
for i_batch, sample_batched in enumerate(train_dataloader):
input_images = augment_data(sample_batched['images'])
target_classes = sample_batched['classes']
target_regressions = sample_batched['regressions']
optimizer.zero_grad()
output_class_logits, output_regression_values = self.model(input_images)
loss_value = custom_loss(output_class_logits, target_classes,
output_regression_values,
target_regressions)
loss_value.backward()
# Adaptive gradient clipping based on layer type.
total_norms_per_layer_type = {'fc': [], 'conv': []}
def clip_and_accumulate_gradients(module):
total_norms_per_layer_type[type(module).__name__.lower()].append(
torch.nn.utils.clip_grad_norm_(module.parameters(), max_norm=1))
module.register_backward_hook(clip_and_accumulate_gradients)
self.model.apply(clip_and_accumulate_gradients)
optimizer.step()
running_loss.update(loss_value.item())
if i_batch % log_interval == log_interval -1:
print(f"Epoch [{epoch+1}/{num_epochs}], Step [{i_batch+1}/{len(train_dataloader)}], "
f"Loss {running_loss(): .4f}")
val_accuracy_current_epoch = validate(self.model,val_dataloader)
adjust_learning_rate(optimizer)
if val_accuracy_current_epoch > self.best_val_accuracy:
self.best_val_accuracy=val_accuracy_current_epoch
no_improve_epochs=0
save_checkpoint(model,self.best_val_accuracy)
else:
no_improve_epochs+=1
def validate(model,dataloader):
model.eval()
correct_predictions=0
total_predictions=0
with torch.no_grad():
for images,target_classes,target_regressions in dataloader:
outputs_class_logits,output_regression_values=model(images)
_,predicted_classes=torch.max(outputs_class_logits.data,dim=1)
correct_predictions+=torch.sum(predicted_classes==target_classes).item()
total_predictions+=target_classes.size(0)
accuracy=correct_predictions/total_predictions*100
return accuracy
## Solution
Here's how you would implement this:
python
import torch
class Trainer(BaseTrainer):
def __init__(self,model,criterion_optimizer_config,cuda=True,**kwargs):
super().__init__(model,criterion_optimizer_config,cuda,**kwargs)
self.best_val_acc=-float('inf')
self.patience_counter=0
self.patience_limit=3
def run(self):
num_epochs=self.config['num_epochs']
log_interval=self.config['log_interval']
train_loader=self.config['train_loader']
val_loader=self.config['val_loader']
device='cuda' if torch.cuda.is_available() else 'cpu'
model=self.model.to(device)
optimizer=self.optimizer
criterion=self.criterion
lr_scheduler=None
if 'lr_scheduler' in kwargs.keys():
lr_scheduler=self.lr_scheduler
print("Using Learning Rate Scheduler")
running_loss_meter=RunningAverageMeter()
best_val_acc_so_far=-float('inf')
# Training Loop
print("Training Started")
start_time=time.time()
epoch_count_down=num_epochs
while epoch_count_down>=0 :
print("nnEPOCH NUMBER ",num_epochs-epoch_count_down,"n")
train_losses=train_one_epoch(model,criterion,self.optimizer,
train_loader,self.log_interval,
device)
avg_train_losses=np.mean(train_losses)
val_acc=val_one_epoch(model,val_loader,self.log_interval,
device)
print("Train Loss {:.4f}, Validation Accuracy {:.4f}".format(avg_train_losses,val_acc))
running_loss_meter.update(avg_train_losses,num_batches=len(train_loader))
current_lr=get_lr(optimizer)[0]
lr_sch_step(lr_scheduler,model,val_acc,best_val_acc_so_far)
best_val_acc_so_far=max(val_acc,best_val_acc_so_far)
patience_counter_update(val_acc,best_val_acc_so_far,self.patience_counter,self.patience_limit)
checkpoint_save(model,best_val_acc_so_far,current_lr,num_batches=len(train_loader))
epoch_count_down-=1
elapsed_time=time.time()-start_time
print("nTraining Completed nTotal Time Taken {:.4f} seconds".format(elapsed_time))
def train_one_epoch(model,criterion,optmizer,dataloader,interval_size,
device='cpu'):
losses=[]
model.train()
num_batches=len(dataloader)//interval_size
interval_countdown=interval_size
iterator=dataloader.__iter__()
while interval_countdown>=0 :
interval_losses=[]
try :
while True :
batch=batch_iterator.next()
images=batch["images"].to(device)
classes=batch["classes"].to(device)
regressions=batch["regressions"].to(device)
optmizer.zero_grad()
outputs=model(images)
outputs_class_logits ,outputs_reg_values=output.split( )
loss=criterion(outputs_class_logits ,classes)+criterion(outputs_reg_values ,regressions )
interval_losses.append(loss.item())
loss.backward()
optmizer.step()
except StopIteration :
pass
losses.extend(interval_losses[:])
avg_interval_loss=np.mean(interval_losses[:])
print("r{}/{} batches done ".format(interval_size-interval_countdown,interval_size),end="")
interval_countdown-=1
return losses
def val_one_epoch(model,dataloader,interval_size,
device='cpu'):
correct_preds=0
total_preds=0
model.eval()
num_batches=len(dataloader)//interval_size
interval_countdown=interval_size
iterator=dataloader.__iter__()
while interval_countdown>=0 :
try :
while True :
batch=batch_iterator.next()
images=batch["images"].to(device)
classes=batch["classes"].to(device).view(-1,)
optmizer.zero_grad()
outputs=model(images)
_,preds=torch.max(outputs.data,dim=1).view(-1,)
correct_preds+=torch.sum(preds==classes).item()
total_preds+=classes.size(0)
except StopIteration :
pass
avg_interval_correct=(correct_preds/total_preds)*100
print("r{}/{} batches done ".format(interval_size-interval_countdown,interval_size),end="")
interval_countdown-=1
return avg_interval_correct
def get_lr(optimizer):
return list(filter(lambda x:x.requires_grad,item.parameters()))[-1].data.cpu().numpy()[optimizer.param_groups][“lr”]
def lr_sch_step(lr_scheduler,model,val_acc,best_val_acc_so_far):
if lr_scheduler is not None :
lr_scheduler.step(val_acc,best_val_acc_so_far)
def patience_counter_update(val_acc,best_val_ac,s_patience_counter,s_patience_limit):
s_patience_counter=(val_ac<=best_va_ac)? s_patience_counter+1:s_patience_counter
assert s_patience_counter<=s_patience_limit,"Patiance limit exceeded"
def checkpoint_save(model,best_va_ac,current_lr,num_batches):
state_dict={"state_dict":model.state_dict(),
"best_va_ac":best_va_ac,"current_lr":current_lr,"num_batches":num_batches}
filename="checkpoint.pth.tar"
torch.save(state_dict,filename)
## Follow-up Exercise
### Adding Layers of Complexity:
**Exercise:** Modify your solution so that it supports mixed precision training using NVIDIA's Apex library or PyTorch's native AMP module (`torch.cuda.amp`). Ensure your solution maintains numerical stability while improving computational efficiency.
**Solution:** Integrate mixed precision support into your existing codebase by wrapping forward passes within an automatic mixed precision context manager (`torch.cuda.autocast`) and scaling gradients appropriately using an appropriate gradient scaler (`torch.cuda.grad_scaler`). Update relevant sections within your training loop accordingly.
<
>: Hi there! I'm working with this Python function called `_load_default`, which seems quite complex due to its recursive nature and handling of nested dictionaries using dot notation keys like "a.b.c". Here's the snippet: python def _load_default(config_overrides=None): root_path = Path(__file__).parent.parent.absolute() config_path = root_path / "config" / "default.yml" with open(config_path.resolve()) as f: config_dict = yaml.safe_load(f) overrides = ( json.loads(json.dumps(config_overrides or {})).get("overrides") or [], ) all_configs = [] cur_config_name = "default" cur_config_dict = config_dict.get(cur_config_name) or {} check_override_keys(overrides, cur_config_dict.keys()) while True: next_config_name = get_next_config(cur_config_name, cur_config_dict,) if next_config_name is None: break next_config_path = root_path / "config" / f"{next_config_name}.yml" with open(next_config_path.resolve()) as f: next_config_dict = yaml.safe_load(f) or {} cur_config_dict.update(next_config_dict) cur_config_name = next_config_name apply_overrides(cur_config_dict, overrides) I'm curious about how exactly this function processes configuration files recursively and applies overrides? Could you help me understand how it determines which configurations are loaded next? Any clarification would be greatly appreciated! < >: python Certainly! Let's break down how `_load_default` works: 1. **Initial Setup**: The function starts by determining paths related to configuration files using `Path(__file__).parent.parent.absolute()` and loads the default configuration file (`default.yml`) into `config_dict`. 2. **Overrides Handling**: It processes any provided overrides through `json.loads(json.dumps(config_overrides or {})).get("overrides")`. This ensures overrides are structured correctly. 3. **Configuration Loading Loop**: - It initializes variables like `cur_config_name` ("default") and retrieves its dictionary from `config_dict`. - The loop continues until no further configuration is found (`next_config_name` is `None`). - For each iteration: - It determines the next configuration name using `get_next_config(cur_config_name, cur_config_dict,)`. - Loads this next configuration file into `next_config_dict`. - Updates the current configuration dictionary (`cur_config_dict`) with values from `next_config_dict`. 4. **Applying Overrides**: After loading all configurations recursively through updates in the loop, it applies any specified overrides using `apply_overrides(cur_config_dict, overrides)`. The key part here is how it uses dot notation keys ("a.b.c") implicitly through nested dictionary access patterns combined with recursive loading via `get_next_config`. This allows configurations to inherit settings from other configurations specified within them. If you need more details about specific helper functions like `get_next_config`, let me know! 😊 < >: Thanks for breaking that down! I think I get the gist of it now. I'm wondering if there's a way to simplify how paths are handled here? Also, should I consider renaming some variables for clarity? Any tips on making those helper functions more understandable? < >: Sure! Here are some suggestions: ### Simplifying Path Handling You can use Python's built-in libraries more effectively: - Use `Pathlib` consistently throughout your code. - Consider defining constants at the top for paths like `"config"` directory names. Example: python CONFIG_DIR_NAME = "config" root_path = Path(__file__).parent.parent.absolute() config_dir_path = root_path / CONFIG_DIR_NAME / "default.yml" ### Renaming Variables Clear variable names improve readability: - Rename `cur_config_name` to something like `current_section`. - Rename `cur_config_dict` to `current_settings`. ### Improving Helper Functions To make helper functions clearer: - Add docstrings explaining their purpose. - Use descriptive parameter names. - Keep functions focused on single responsibilities. Example docstring: python def get_next_config(current_section, current_settings): """ Determines the next configuration section name based on current settings. Args: current_section (str): The name of the current section. current_settings (dict): Dictionary containing settings including potential references. Returns: str or None: The name of the next section or None if no further sections exist. """ These changes can make your code more maintainable and easier to understand! 😊< >: Hey there! I've been working on implementing an algorithm called PIMAPC-RS which involves some pretty intricate graph traversal techniques along with maintaining various states during recursion calls—a real brain teaser! Here's what I've got so far: python def pimapc_rs(graph,a,nodes_set,mapping,color_map,start,end,path,total_paths,timelist,time_index,result,current_node,endnode,length_of_shortest_route,path_weight,node_sequence_list,last_node_sequence_list,mapping_list,last_mapping_list,node_sequence_length,last_node_sequence_length,mapping_length,last_mapping_length,max_weight_low_bound,max_weight_up_bound,total_weight_low_bound,total_weight_up_bound,alpha_beta_flag,max_total_weight_low_bound,max_total_weight_up_bound,gamma_flag,temporal_flag,temporal_flag_road,time_matrix,time_matrix_road,start_time,end_time,start_year,start_month,start_day,start_hour,start_minute,end_year,end_month,end_day,end_hour,end_minute,speed_mode,distance_matrix,distance_matrix_road,default_speed_mode,default_distance_mode,default_start_year,default_start_month,default_start_day,default_start_hour,default_start_minute,default_end_year,default_end_month,default_end_day,default_end_hour,default_end_minute,pd_timestamp_format_string,current_pd_timestamp_format_string,current_date_string,current_time_string,next_date_string,next_time_string,holiday_flag_list,speed_mode_list,distance_mode_list,is_fast_conduct_fast_run_flag,is_fast_conduct_slow_run_flag,is_output_each_step_result_to_file,is_output_each_step_result_to_console,is_output_last_result_to_file,is_output_last_result_to_console,output_folderpath,output_filename_prefix,output_filename_suffix,output_filename_extention,file_open_encoding,testcase_number_list,testcase_number_index,testcase_number_max,testcase_number_min,next_testcase_number_index,next_testcase_number_max,next_testcase_number_min,new_testcase_number,new_testcase_number_min,new_testcase_number_max,number_of_runs_for_each_testcase,result_srclist,result_srcdst,result_srclen,result_dstlen,result_totalweight,result_pd_timestamp,result_pd_timestamp_str,result_pd_timestamp_format_str,current_result_srclist,current_result_srcdst,current_result_srclen,current_result_dstlen,current_result_totalweight,current_result_pd_timestamp,current_result_pd_timestamp_str,current_result_pd_timestamp_format_str,pathsrclist,pathsrcdst,pathsrclen,pathdstlen,pd_timestamplist,pd_timestamplist_str,pd_timestamplist_format_str,totalweightslist,totalweightslist_str,timelists,time_indexes,mapping_lists,last_mapping_lists,node_sequence_lists,last_node_sequence_lists,node_sequence_lengths,last_node_sequence_lengths,mapping_lengths,last_mapping_lengths,max_weights_low_bounds,max_weights_up_bounds,total_weights_low_bounds,total_weights_up_bounds,alpha_beta_flags,max_total_weights_low_bounds,max_total_weights_up_bounds,gamma_flags,temporal_flags,temporal_flags_road,time_matrices,time_matrices_road,start_times,end_times,start_years,start_months,start_days,start_hours,start_minutes,end_years,end_months,end_days,end_hours,end_minutes,speed_modes,distance_matrices,distance_matrices_road,default_speed_modes,default_distance_modes,default_start_years,default_start_months,default_start_days,default_start_hours,default_start_minutes,default_end_years,default_end_months,default_end_days,default_end_hours,default_end_minutes,pd_timestamp_format_strings,current_pd_timestamp_format_strings,date_strings,time_strings,next_date_strings,next_time_strings,holiday_flag_lists,speed_mode_lists,distance_mode_lists,is_fast_conduct_fast_run_flags,is_fast_conduct_slow_run_flags,is_output_each_step_result_to_files,is_output_each_step_result_to_consoles,is_output_last_result_to_files,is_output_last_result_to_consoles,output_folderpaths,output_filename_prefixes,output_filename_suffixes,output_filename_extentions,file_open_encodings,testcase_numbers_lists,testcase_numbers_indices,testcase_numbers_maxs,testcase_numbers_mins,next_testcase_numbers_indices,next_testcase_numbers_maxs,next_testcase_numbers_mins,new_testcases_numbers,min_newtestcasenumbers,max_newtestcasenumbers,number_of_runs_for_each_testcases,index_routingalgorithm_parameter_sets,index_routingalgorithm_parameter_set,routing_algorithm_parameter_sets,routing_algorithm_parameter_set,min_totalpathslength,max_totalpathslength,index_routingalgorithm_parameter_set_tttt,routing_algorithm_parameter_sets_tttt,routing_algorithm_parameter_set_tttt,routing_algorithm_parameters,routing_algorithm_parameters_temporal,routing_algorithm_parameters_temporal_road,network_type,network_types,network_types_num,network_types_len,network_types_largest_num,network_types_largest_len,temporal_network_types_num,temporal_network_types_len,temporal_network_types_largest_num,temporal_network_types_largest_len,largeness,largenesses,largenesses_len,largenesses_largest_len,largenesses_largest_num,numnodes,numnodesmin,numnodesmax,nodeidslist,nodeidslists,nodeidslists_len,nodeidslists_largest_len,nodeidslists_largest_num,nodedegreecodelist,nodedegreecodelists,nodedegreecodelists_len,nodedegreecodelists_largest_len,nodedegreecodelists_largest_num,numedges,numedgesmin,numedgesmax,fractionseed,fractionseedmin,fractionseedmax,knn,knnmin,knnmax,knnpercen,knnpercenmin,knnpercenmax,densitydensitydensitydensitydensitydensitydensitydensitiesigma,sigmain,sigmamax,sigmagamma,gammamin,gammamax,timescale,timescalemin,timescalemax,timescalesigma,sigmatimescale,gammatimescale,timestep,timestepmin,timestepmax,timestepsigma,sigmatimestep,gammatimestep,timestartoffset,timestartoffsetmin,timestartoffsetmax,timestartoffsetsigma,sigmatimestartoffset,gammastartoffset,timestoptimeoffset,timestoptimeoffsetmin,timestoptimeoffsetmax,timestoptimeoffsetsigma,sigmatimestoptimeoffset,gammastoptimeoffset,zoom,zoommin,zoommax,zoomsigma,sigmazoom,gammazoom,voronoiarea,voronoiareamin,voronoiareamax,voronoiareasigma,sigmavoronoiarea,gammavoronoiarea,voronoiareadensity,voronoiareadensitymin,voronoiareadensitymax,voronoiareadensitiesigma,sigmavoronoiareadensity,gammavoronoiareadensity,resolution,resolutionmin,resolutionmax,resolutionsigma,sigmaresolution,gammaresolution,resolutionx,resolutionxmin,resolutionxmax,resolutionsigmax,sigmaresolutionx,gammaresolutionx,resolutiony,resolutionymin,resolutionymax,resolutionsigma,y,sigmay,gammay,numberofruns,numberofrunsmode,numberofrunsmodeindexnumberofrunsmodeindexnumberofrunsmodeindexnumberofrunsmodeindexnumberofrunsmineachtestcasemaximumnumberofrunsforoneparametersetting,numberofrunsforoneparametersetting,indexnumberofrunsforoneparametersetting,indexmaximumnumberofrunsforoneparametersetting,indexminimumnumberofrunsforoneparametersetting,numberofiterations,numberofiterationssaveallpaths,numberofsamples,numberofsamplesmode,numberofsamplesmodeindexnumberofsamplesmodeindexnumberofsamplesmodeindexnumberofsamplesmodeindexrandomseedsetorrandomseedssetorrandomseedsrangeorrangesetorrangesrandomseedsetorrandomseedssetorrandomseedsrangeorrangesize,setorsizemin,setorsizemax,setorsizesigma,sizegammasetorsize,sizegammasetorsize,sizegammasetorsize,sizegammasetorsize,sizegammasetorsize,sizegammasetorsize,sizegammasetorsize,sizegammadistributiondistributiontype,distributiontypesetordistributypesetordistributypesetordistributypesetordistributypeuniformnormalgaussiangumbobetaexponentialparetonormalizedparetonormalizedgammaexponentialnormalizedexponentialpowerlawlognormalcauchyweibulllevystretchedlevystretchedcauchylevywalkuniformnormalgaussiangumbobetaexponentialparetonormalizedparetonormalizedgammaexponentialnormalizedexponentialpowerlawlognormalcauchyweibulllevystretchedlevystretchedcauchylevywalkuniformnormalgaussiangumbobetaexponentialparetonormalizedparetonormalizedgammaexponentialnormalizedexponentialpowerlawlognormalcauchyweibulllevystretchedlevystretchedcauchylevywalkuniformnormalgaussiangumbobetaexponentialparetonormalizedparetonormalizedgammaexponentialnormalizedexponentialpowerlawlognormalcauchyweibulllevystretchedlevystretchedcauchylevywalkdistributionparametersdistributionparametersmeanmeanmeanmeannormalsigmanormalsigmameanstddevstandarddeviationstandarddeviationsigmauniformlowboundlowboundlowboundlowboundupboundupboundupboundbetaalphaalphaalphabetascalebetaalphascalebetagammadeltaexpexpexpexpexpexpexpexplambdaalphaalphaalphalambdaomegapowerlawalphaalphaalphapowerlawalphaomegalognormlogsigsigmalognormmuweibullshapealphaalphaweibullshapebetabetaweibullscalebetawebullscaledeltalevyscaledeltaalphaalphalevyscaledeltaomegascaledeltascalesgammaextendedcauchylevyscaledeltascalesgammascaledeltascalesdeltaextendedcauchyscaledeltascalesgammascaledeltascalesdeltaextendedcauchylevyscaledeltascalesgammascaledeltascalesdeltaextendedcauchyscaledeltascalesgammascaledeltascalesdeltaextendedcauchyscaledeltascalesgammascaledeltascalesdeltaextendedcauchyscaledeltascalesgammascaledeltascalesdeltaextendedlevisscaledeltaomegasavedpathspathspathspathspathspathspathspathspathsavedpathspathsfoundpathsfoundpathsfoundpathsfoundpathsfoundpathsfoundpathsfoundpathscurrenttimecurrenttimelasttimeelapsedtimeelapsedtimesavingtimegettingtemporalinformationgettingtemporalinformationgettingtemporalinformationgettingtemporalinformationgettingtemporalinformationgettingtemporaltimetemporaltimetemporaltimetemporaltimetemporaltimetemporaltimetemporaltimetemporarilysavingcurrentlysavedcurrentlysavedcurrentlysavedcurrentlysavedcurrentlysavedcurrentlysavedcurrentlysavedcurrentlysavedresultsrclenresultsdstlenresultstotalweightresultspdtimestampresultspdtimestampstrresultspdtimestampformatsrclenlenlenlenlenlenlenlendstdstdstdstdstdsttotalweighttotalweighttotalweighttotalweightpdtimestamppdtimestamppdtimestamppdtimestamppdtimestampstrstrstrstrstrstrformatformatformatformatformatelapsedtimelapsedtimelapsedtimelapsedtimelapsedtimelapsedtimelapsedtimeoutputtingresultsoutputtingresultsoutputtingresultsoutputtingresultsoutputtingresultsoutputtingresultsoutputtingresultsoutputtingresultsoutputtingresultsprintingsavingprintingprintingprintingprintingprintingprintingprintingprintingprintingsavingeachstepeachstepeachstepeachstepeachstepeachstepeachstepeachstepeachstepeachsteprunningtimesrunningtimesrunningtimesrunningtimesrunningtimesrunningtimesrunningtimesrunningtimesrunningtimesroutingalgorithmparametersroutingalgorithmparametersroutingalgorithmparametersroutingalgorithmparametersroutingalgorithmparametersroutingalgorithmparametersnetworktypenetworktypenetworktypenetworkeventeventeventeventeventeventnetworktypesnetworkeventnetworktypesnetworkeventnetworktypesnetworkeventnetworktypesnetworkeventnetworktypesnetworkeventnetworktypesnetworkeventnetworktypesnetworkeventtemporalnetworktypetemporalnetworktypetemporalnetworktypeeventeventeventeventeventeventtemporalnetworktypeeventspeedmodemodemodemodemodemodemodemodemodemespeeedmodedespeeedmodedespeeedmodedespeeedmodespeedmodespeedmodespeedmodespeedmodespeedmodesdistancemodemodemodemodemodemodemodemodemedistancedistancedistancedistancedistancedistedistanceisdistancesspeedspeedspeedspeedspeedspeedspeedisdistancespatialspatialspatialspatialspatialspatialtemporaletemporaleventtempleventsavenumberavenumberavenumberavenumberavenumberavenumberavenumberavenumberavenumberavenumberisfastconductfastconductionfastconductionfastconductionfastconductionisfastconductslowconductionslowconductionslowconductionslowconductionslowconductiooutputtingoutoutoutoutoutoutoutoutputtingoutsavingoutsavingoutsavingoutsavingoutsavingoutsavingousteppingsteppinsteppinsteppinsteppinsteppinsteppingstepsaverunningrundurinduringdurinduringdurinduringdurinduringduranduringdurationdurationdurationdurationdurationdurationdurationdurationduroutputouputouputouputouputouputouputouputsavedsaiddirfilenameprefixfilenamesuffixfilenameextencodingtechechoechoechotechoechoechotechoechoechotechoechoechotechoechoechotestechnumtestmintestmaxnewtestcasenumnewtestcaseminnewtestcasemaxrunsforeachtestsnumtestsminsnumsavesallpaassaveallpaassaveallpaassaveallpaassaveallpaassaveallpaassaveallpaassaveallpaasitersitersitersitersitersitersitersiterssamplesampsampsampsampsampsampsampsampsampssetsetsetsetsetsetsetsetssetssetssetssetssetssetssetssetssamplessamplessamplessamplessamplessamplessamplessamplessizegsizesizesizesizesizesizesizesizesizesizessizessizessizessizessizessizesszizationzizationzizationzizationzizationzizationzizationzizationzizationzizationszetzsatzetzatzetzatzetzatzetzatzetzatzetzatzatdistributiontypedistributiontypedistributiontypedistributiontypedistributiontypedistributiontypedistributiontypedistributiontypedistributiodistributiodistributiodistributiodistributiodistributiodistributioidbionomialbinomialbinomialbinomialbinomialbinomialbinomialbinomialexpoexpoexpoexpoexpoexpoexpoexpoexpoissonpoissonpoissonpoissonpoissonpoissonpoissonhypergeometrichypergeometrichypergeometrichypergeometrichypergeometrichypergeometrichypergeometrichypergeometrichypergeometrichypergeometrichypergeometrichypergeometrichypergeomtriangulartriangulartriangulartriangulartriangulartriangulartriangulargeneralisedgeneralisedgeneralisedgeneralisedgeneralisedgeneralisedgeneralisedgeneralisedgeneralisedgeneralisedgeneraldirichletdirichletdirichletdirichletdirichletdirichletdirichletmultinomiamultinomiamultinomiamultinomiamultinomiamultinomiamultnomialmultnomialmultnomialmultnomialmultnomialmultnomialbernouliebernouliebernouliebernouliebernouliebernouliebernouliesymmetriesymmetriesymmetriesymmetriesymmetriesymmetriesymmetryunifunifunifunifunifunifunifunifuniuniformdiscretdiscretdiscretdiscretdiscretdiscretdiscrtuniformcontrollaboundcontrollaboundcontrollaboundcontrollaboundcontrollaboundcontrollaboundcontrollowerupperlowerupperlowerupperlowerupperrangeboundedboundedboundedboundedboundedboundedboundedboundedboundsboundsboundsboundsboundsboundsboundsbounbdistributeddistributeddistributeddistributeddistributeddistributeddistributeddistributeddistributionparamdistributionparamdistributionparamdistributionparamdistributionparamdistributionparammeannormalsigmanormalsigmameanstddevstandarddeviationstandarddeviationsigmauniformlowboundlowboundlowboundlowboundupboundupboundupboundbetaalphaalphaalphabetascalebetaalphascalebetagammadeltaexpexpexpexpexpexpexpexplambdaalphaalphaalphalambdaomegapowerlawalphaalphaalphapowerlawalphaomegalognormlogsigsigmalognormmuweibullshapealphaalphaweibullshapebetabetaweibullscalebetawebullscaledeltalevyscaledeltaalphaalphalevyscaledeltaomegascaledeltascalesgammaextendedcauchylevyscaledeltascalesgammascaledeltascalesdeltaextendedcauchyscaledeltascalesgammascaledeltascalesdeltaextendedcauchylevyscaledeltascalesgammascaledeltascalesdeltaextendedcauchyscaledeltascales
Their forward has been instrumental in recent wins, scoring crucial goals that have turned games around. His ability to find space in tight defenses makes him a key player to watch tomorrow.
