Laboral Kutxa Baskonia: ACB League Stars & Stats

Team Playing Style and Tactics

Baskonia is renowned for its disciplined playing style, often employing a 1-5-5 formation that emphasizes strong defense and efficient ball movement. Their strategies focus on exploiting mismatches and maintaining high tempo play. However, their reliance on structured plays can sometimes make them predictable against well-prepared opponents.

Interesting Facts and Unique Traits

Baskonia’s nickname, “The Lions,” reflects their fierce competitiveness. The team enjoys a passionate fanbase known as “Los Cimbres.” They have rivalries with teams like Real Madrid and FC Barcelona Lassa, adding excitement to their matches. Traditions such as the “Baskoniano Day” highlight their deep-rooted connection with fans.

Lists & Rankings of Players & Stats

• Dylan Ennis: Top scorer ✅
• Jarrius Jackson: Versatile playmaker 🎰
• Adam Hanga: Defensive anchor 💡

Comparisons with Other Teams in the League or Division

Basketball analysts often compare Baskonia’s tactical discipline with teams like Real Madrid’s star-studded lineup or Barcelona’s dynamic offense. While these teams may have more individual talent, Baskonia’s cohesive unit often gives them an edge in tightly contested matches.

Case Studies or Notable Matches

A key victory worth analyzing is their 2019 win against FC Barcelona Lassa in the Copa del Rey final. This match showcased Baskonia’s resilience and strategic depth, making it a valuable case study for bettors looking to understand their potential in knockout games.

Tips & Recommendations for Betting Analysis 💡 Advice Blocks

• Analyze head-to-head records against key rivals to gauge performance trends.
• Monitor player injuries closely as they can significantly impact game outcomes.
• Evaluate recent form over several games rather than isolated performances.

Quotes or Expert Opinions about the Team (Quote Block)

“Baskonia’s strength lies in their unity and tactical discipline,” says renowned basketball analyst John Doe.

Pros & Cons of the Team’s Current Form or Performance (✅❌ Lists)

• ✅ Strong defensive setup that limits opponent scoring opportunities.
• ✅ Cohesive team play that enhances overall effectiveness on court.

• ❌ Occasionally struggles against teams with superior individual talent.
• ❌ Can become predictable if opponents decipher their tactical patterns early on.</li
  <>: Hi there! I’m working on implementing an attention mechanism using PyTorch, specifically focusing on computing attention weights from query vectors (`q`), key vectors (`k`), value vectors (`v`), an optional mask (`mask`), dropout probability (`dropout_p`), and an optional scaling factor (`scale`). Here’s my code:

  python
  def forward(self, q: torch.Tensor,
  k: torch.Tensor,
  v: torch.Tensor,
  mask: Optional[torch.Tensor] = None) -> torch.Tensor:
  batch_size_q, seq_len_q = q.size()
  batch_size_k, seq_len_k = k.size()
  assert batch_size_q == batch_size_k == self.batch_size
  dim_per_head = self.d_v // self.n_heads
  q = self.W_q(q).view(batch_size_q, seq_len_q,
  self.n_heads,
  dim_per_head).transpose(1, 2)

  k = self.W_k(k).view(batch_size_k, seq_len_k,
  self.n_heads,
  dim_per_head).transpose(1, 2)

  v = self.W_v(v).view(batch_size_k, seq_len_k,
  self.n_heads,
  dim_per_head).transpose(1, 2)

  att = q @ k.transpose(-2, -1)

  if self.scale:
  att = att / (dim_per_head ** 0.5)

  # Bug introduced here: incorrect mask shape handling
  if mask is not None:
  att = att.masked_fill(mask[:, :, :seq_len_q], -np.inf)

  att = nn.Softmax(dim=-1)(att)
  if self.dropout_p > 0:
  att = nn.Dropout(self.dropout_p)(att)

  out = att @ v

  out = out.transpose(1, 2).contiguous()
  .view(batch_size_q, seq_len_q, dim_per_head * self.n_heads)

  return out

  However, when I run this code with certain inputs where `mask` has different dimensions than expected due to broadcasting issues during masking operation I get this error:

  Traceback (most recent call last):
  File “attention.py”, line 27, in forward
  att = att.masked_fill(mask[:, :, :seq_len_q], -np.inf)
  RuntimeError: The size of tensor a (8) must match the size of tensor b (10) at non-singleton dimension 3

  I’m really puzzled by this error because I thought I handled dimensions correctly while reshaping tensors before applying masks. Could you help me figure out what might be going wrong here? Thanks!
  <>: python
  It looks like the issue arises from how you’re handling the mask dimensions during the `masked_fill` operation. The error indicates a mismatch between the dimensions of `att` and `mask`.

  Here’s how you can address it:

  1. Ensure that your mask tensor has compatible dimensions with `att`. Specifically, it should match `(batch_size_q * n_heads)` along dimension `-3` after transposing.

  2. You might need to expand or unsqueeze your mask tensor to match these dimensions.

  Here’s a possible fix:

  python
  if mask is not None:
  # Assuming mask is of shape [batch_size_q, seq_len_q]
  # We need to expand it to [batch_size_q * n_heads]

  # Expand dimensions for heads
  mask = mask.unsqueeze(1).unsqueeze(0) # Shape becomes [1, 1, batch_size_q, seq_len_q]

  # Repeat across heads
  mask = mask.repeat(self.n_heads * batch_size_q // batch_size_q + 1,
  seq_len_k // seq_len_q + 1,
  1,
  1)[:self.n_heads * batch_size_q]

  # Apply masked_fill
  att = att.masked_fill(mask == False.unsqueeze(-1), -np.inf)

  This approach ensures that your mask aligns correctly with `att`. Make sure your initial mask shape aligns with `[batch_size_q]` before expanding it.

  Let me know if this helps! 😊

  <>: Thanks for the suggestion! I see how expanding and repeating the mask could solve the dimension mismatch issue. However, I’m concerned about potential inefficiencies due to excessive repetition of data across heads. Is there a way to handle this more efficiently without repeating unnecessarily? Also, should I consider any edge cases where this approach might fail?
  <>: To handle this more efficiently without excessive repetition:

  You can use broadcasting instead of repeating by ensuring that both `att` and `mask` are compatible through broadcasting rules:

  python
  if mask is not None:
  # Assuming mask is of shape [batch_size_q, seq_len_q]

  # Expand dimensions for heads without repeating data unnecessarily
  mask = mask.unsqueeze(1).unsqueeze(0) # Shape becomes [1, 1, batch_size_q, seq_len_q]

  # Apply masked_fill using broadcasting
  att = att.masked_fill(mask == False.unsqueeze(-1), -np.inf)

  ### Considerations:
  – **Broadcasting**: Ensure that both tensors are compatible for broadcasting by aligning dimensions properly.
  – **Edge Cases**:
  – If `mask` has fewer dimensions than expected initially (e.g., missing batch dimension), ensure it’s correctly expanded.
  – Handle cases where `seq_len_k != seq_len_q`, ensuring proper alignment.

  This approach leverages PyTorch’s broadcasting capabilities to avoid unnecessary data duplication while maintaining efficiency.
  <>: Got it! Using broadcasting seems like a smart move to avoid unnecessary repetition. Just to clarify—if I understand correctly—this means I don’t need to repeat the mask across heads explicitly? Also, are there specific scenarios where broadcasting might not work as expected? For instance, if `seq_len_k` differs significantly from `seq_len_q`, could that cause issues?
  <>: Exactly! With broadcasting:

  – You don’t need to explicitly repeat the mask across heads; PyTorch will handle it automatically as long as dimensions align correctly.

  ### Scenarios Where Broadcasting Might Not Work:

  – **Dimension Mismatch**: Ensure all necessary dimensions are aligned before applying operations.

  – **Significant Differences in Sequence Lengths**: If `seq_len_k` differs greatly from `seq_len_q`, ensure your logic accounts for these differences when aligning masks.

  – **Mask Shape**: If your initial mask doesn’t cover all required dimensions (e.g., missing head dimension), make sure you expand it appropriately.

  By carefully managing these aspects, you can leverage broadcasting effectively without running into issues.
  <>: Great clarification! So just confirming—by using broadcasting correctly aligned with PyTorch’s rules should handle everything without explicit repetition across heads? Also regarding sequence length differences—if `seq_len_k` is much larger than `seq_len_q`, do I need additional logic to ensure everything aligns properly when applying masks?
  <>: Yes! By aligning dimensions according to PyTorch’s broadcasting rules:

  – You avoid explicit repetition across heads; PyTorch manages it automatically.

  Regarding sequence length differences:

  – If `seq_len_k` is much larger than `seq_len_q`, ensure your logic accounts for these differences when applying masks.

  – You might need additional logic if operations depend on specific alignments between sequences (e.g., padding or trimming).

  By carefully setting up your tensors’ shapes before operations like masking or multiplication will help maintain alignment naturally through broadcasting.
  <>: Perfect! That clears things up about avoiding explicit repetition with broadcasting. Now about those sequence length differences—if `seq_len_k` is significantly larger than `seq_len_q`, should I consider padding or trimming sequences before applying masks? Or does broadcasting handle such discrepancies automatically?
  <>: Broadcasting itself doesn’t automatically handle discrepancies like padding or trimming; it assumes compatible shapes once aligned.

  For significant differences between `seq_len_k` and `seq_len_q`, consider these steps:

  – **Padding**: Ensure sequences are padded uniformly so they match expected lengths during operations.

  – **Trimming**: Trim sequences if necessary before processing them together.

  These adjustments ensure proper alignment before applying masks or other operations using broadcasting.
  <>: Awesome! So if I pad or trim sequences beforehand to match lengths where needed before applying masks or other operations using broadcasting—I should be good? Just want to make sure I’m not missing any crucial step here.
  <>: Exactly! By padding or trimming sequences beforehand:

  – You ensure compatibility in lengths before applying operations like masking.

  This preparation allows you to leverage PyTorch’s broadcasting effectively without running into alignment issues during computation.

  Just double-check that all tensors involved have compatible shapes after these adjustments. You’re all set then! 😊<>: Hi there,

  I’m trying to implement a method called `_compute_subnetwork_edge_weights_from_nei_info`. This method processes neighbor information from various files within directories named ‘iteratively_determined_paths’. It computes edge weights based on shortest path distances between nodes retrieved from files named ‘nodesInfo.csv’ within each directory path specified by an index list.

  However something seems off when running my code—it doesn’t behave as expected—and now throws an error which leaves me quite puzzled!

  Here’s my buggy version of code along with the traceback error message:

  python
  def _compute_subnetwork_edge_weights_from_nei_info(self):
  #
  print(“—- Computing edge weights”)
  row_col_dict = {}
  weighted_edge_list_dict = {}
  total_n_neighbors_in_network_iterativelydetermined={}
  sys.stdout.flush()
  for iteratioin_number,pwd in enumerate(self.pwdlist):
  filename=’nodesInfo.csv’
  gi=pd.read_csv(opj(pwd,filename))
  n_neighbors=gi.shape[0]-1
  total_n_neighbors_in_network_iterativelydetermined[iteratioin_number]=n_neighbors
  for CurrNodeNbrPairIndex,row in gi.iterrows():
  i=row[‘Source Node’]
  j=row[‘Target Node’]
  d=float((row[‘Length’]))
  row_col_str=str(i)+” “+str(j)
  row_col_str_tup=(i,j)
  if not row_col_str_tup in weighted_edge_list_dict.keys():
  weighted_edge_list_dict[row_col_str_tup]=d
  row_col_dict[row_col_str]=row_col_str_tup
  sys.stdout.flush()
  sum_of_all_shortest_pathes=0
  weighted_edge_list=[]
  nEdgesInNetwork=0
  len_row_col_dict=len(row_col_dict)
  print(“number_of_edges=”+str(len_row_col_dict))
  for row_index,(key,value) in enumerate(row_col_dict.items()):
  sum_of_all_shortest_pathes+=weighted_edge_list_dict[value]
  nEdgesInNetwork+=1
  weighted_edge_list.append((key,value,sum_of_all_shortest_pathes))
  sys.stdout.write(“r%d%%” % ((row_index)*100/len_row_col_dict))
  sys.stdout.flush()
  bar_length=50

  And here’s the traceback error message I’m getting:

  Traceback (most recent call last):
  File “my_script.py”, line XX-in _compute_subnetwork_edge_weights_from_nei_info(), line YY
  TypeError: unsupported operand type(s) for +=: ‘int’ and ‘str’

  It seems like there’s some kind of type mismatch happening during summation but I can’t figure out why integers aren’t being summed properly here!

  Could someone please help me identify what might be going wrong?

  Thanks!
  <>: python
  It looks like you’re encountering a type mismatch error because you’re trying to add an integer (`sum_of_all_shortest_pathes`) with a string somewhere in your code. This usually happens when one of your variables isn’t being initialized as an integer or gets reassigned inadvertently.

  Here are some suggestions to fix this issue:

  1. **Initialization Check:** Ensure that `sum_of_all_shortest_pathes` is initialized as an integer at the start of your function:
  python
  sum_of_all_shortest_pathes = 0

  2. **Data Type Consistency:** Verify that all values being added are floats or integers. In particular:
  – Check how values are extracted from your DataFrame (`gi`) using:
  python
  d=float((row[‘Length’]))

  Make sure `’Length’` column always contains numeric strings so conversion doesn’t fail silently elsewhere.

  3. **Debugging:** Add print statements temporarily after extracting values from DataFrame rows:
  python
  print(f”Extracted value d={d}, type={type(d)}”)

  4. **Check Reassignment:** Look through your code for any accidental reassignment of variables used in summation back into strings elsewhere.

  By ensuring consistent data types throughout your calculations and verifying initialization steps carefully should resolve this issue. Let me know if you need further assistance! 😊

  <>: Thanks for pointing that out! I suspect there might be some non-numeric values sneaking into my calculations unexpectedly. To tackle this robustly across multiple iterations over directories/files within ‘iteratively_determined_paths’, should I add some exception handling around file reading/loading? Maybe something like try-except blocks around reading CSV files?

  Also , would adding checks right after extracting values from DataFrame rows help catch any anomalies early on?

  Let me know what you think!
  <>: Adding exception handling around file reading/loading is definitely a good idea. This will help catch errors related to file access or unexpected content formats early on.

  Here’s how you can implement it:

  python
  import pandas as pd

  def _compute_subnetwork_edge_weights_from_nei_info(self):

  print(“—- Computing edge weights”)
  row_col_dict = {}
  weighted_edge_list_dict = {}
  total_n_neighbors_in_network_iterativelydetermined={}

  sys.stdout.flush()

  for iteration_number,pwd in enumerate(self.pwdlist):
  filename=’nodesInfo.csv’
  try:
  gi=pd.read_csv(opj(pwd,filename))
  except Exception as e:
  print(f”Error reading {filename} in {pwd}: {e}”)
  continue

  n_neighbors=gi.shape[0]-1
  total_n_neighbors_in_network_iterativelydetermined[iteration_number]=n_neighbors

  for CurrNodeNbrPairIndex,row in gi.iterrows():
  i=row[‘Source Node’]
  j=row[‘Target Node’]

  try:
  d=float(row[‘Length’])
  except ValueError:
  print(f”Non-numeric value encountered at iteration {iteration_number}, row {CurrNodeNbrPairIndex}: {row}”)
  continue

  row_col_str=str(i)+” “+str(j)
  row_col_str_tup=(i,j)

  if not row_col_str_tup in weighted_edge_list_dict.keys():
  weighted_edge_list_dict[row_col_str_tup]=d
  row_col_dict[row_col_str]=row_col_str_tup

  sys.stdout.flush()

  sum_of_all_shortest_pathes=0
  weighted_edge_list=[]

  nEdgesInNetwork=0

  len_row_col_dict=len(row_col_dict)

  print(“number_of_edges=”+str(len_row_col_dict))

  for row_index,(key,value) in enumerate(row_col_dict.items()):
  sum_of_all_shortest_pathes+=weighted_edge_list_dict[value]
  nEdgesInNetwork+=1
  weighted_edge_list.append((key,value,sum_of_all_shortest_pathes))

  sys.stdout.write(“r%d%%” % ((row_index)*100/len_row_col_dict))
  sys.stdout.flush()
  bar_length=50

  By adding these checks and exception handling blocks around file reading and data extraction processes:

  – You’ll be able to catch file-related errors gracefully.
  – You’ll also identify non-numeric values early on during DataFrame processing.

  This approach will make your code more robust against unexpected data formats or missing files.
  <>: Great suggestions! Adding try-except blocks around file reading seems like a solid plan to catch any file-related issues early on. Also incorporating checks right after extracting values from DataFrame rows should help identify any non-numeric anomalies quickly.

  I’ll implement these changes and see how it goes. If anything else comes up unexpectedly during testing or execution flow changes significantly due to these additions—especially regarding performance impacts—let me know what else might be worth considering!
  <>: Sounds good! Implementing those changes should enhance robustness significantly by catching errors early and preventing unexpected crashes due to data inconsistencies.

  If performance becomes an issue after adding exception handling:

  – Consider logging errors instead of printing them directly; this reduces console clutter.

  – Use Python’s logging module instead of print statements for better control over logging levels and outputs.

  Additionally:

  – Profile your code using tools like cProfile if performance concerns arise post-changes.

  – Keep an eye on memory usage if dealing with large datasets; consider processing data chunks instead of loading entire files into memory at once.

  Feel free to reach out if you encounter further challenges! 😊Implement a python module according to the following instructions:
  Implement a python module according to the following instructions:
  ## General functionality
  The software consists of three main functionalities related to image processing tasks involving segmentation maps used typically in computer vision tasks such as semantic segmentation:

  a) Extracting bounding boxes from segmentation maps based on different modes (‘poly’, ‘rect’, ‘both’).
  b) Calculating Intersection over Union (IoU) scores between predicted segmentation maps (pred_maps) and ground truth segmentation maps (gt_maps).
  c) Converting polygon representations into rectangular bounding boxes centered around objects within images based on segmentation maps.

  ## Specifics and edge cases

  ### Functionality A – Extract Segmentation Maps Function (_extract_segmap_func):

  #### Input Parameters:
  a_mode (‘poly’, ‘rect’, ‘both’): Determines whether polygon coordinates (‘poly’), rectangular coordinates (‘rect’), both (‘both’), none (‘none’), random choice (‘random’), True (‘True’) interpreted as random choice among non-empty results only (‘nonempty_random’), False (‘False’) interpreted as returning empty arrays ([[], []]) should no valid segmentations be found within image bounds.
  b_min_xmin_ymin_xmax_ymax_threshold (-inf): Minimum threshold value allowed per coordinate x_min,y_min,x_max,y_max individually per bounding box found within image bounds below which segmentations will be discarded otherwise kept included upon extraction process result set returned back upon function completion execution conclusion termination ending finish stop end finish wrap up close finish round up complete terminate wrap-up end cap finale finis finalization endgame culmination closure culmination closeout close-up finale finish finale wrap-up conclusion termination endgame culmination closure finale finis finalization endgame culmination closure culmination closeout close-up finale finish finale wrap-up conclusion termination endgame culmination closure culmination closeout close-up finale finish finale wrap-up conclusion termination endgame culmination closure).
  b_max_xmin_ymin_xmax_ymax_threshold (+inf): Maximum threshold value allowed per coordinate x_min,y_min,x_max,y_max individually per bounding box found within image bounds above which segmentations will be discarded otherwise kept included upon extraction process result set returned back upon function completion execution conclusion termination ending finish stop end finish round up complete terminate wrap-up end cap finale finis finalization endgame culmination closure culmination closeout close-up finale finish finale wrap-up conclusion termination endgame culmination closure culmination closeout close-up finale finish finale wrap-up conclusion termination endgame culmination closure).
  b_area_threshold (-inf): Minimum threshold value allowed per area calculated via x_max-x_min times y_max-y_min per bounding box found within image bounds below which segmentations will be discarded otherwise kept included upon extraction process result set returned back upon function completion execution conclusion termination ending finish stop end finish round up complete terminate wrap-up end cap finale finis finalization endgame culmination closure culmination closeout close-up finale finish finale wrap-up conclusion termination endgame culmination closure).

  #### Output Parameters Returned Back Upon Function Completion Execution Conclusion Termination Ending Finish Stop End Finish Round Up Complete Terminate Wrap-Up End Cap Finale Finis Finalization Endgame Culmination Closure Culmination Closeout Close-Up Finale Finish Finale Wrap-Up Conclusion Termination Endgame Culmination Closure Culmination Closeout Close-Up Finale Finish Finale Wrap-Up Conclusion Termination Endgame Culmination Closure):

  xmins ([]): List containing minimum x coordinate values extracted via segmentation map input parameter fed fedded feeded feed fedded fedded fedded feed edfed fdfeed fdeffdeffdeffdefdeff deff def def deff def deff de ff def de ffd effdef def de ffd effdef def de ffd effdef def de ffdeffdef defdeffdefdeffdefdeffdeffdefde ffdefde ffde ffde ff dedf dedf dedf dedf dedf dedf dedf dedf dedf dedfeddfdd efdd ef dd ef dd ef dd ef dd ef dd ef dd ef dd efdd efdd efdd ed ed ed ed ed ed ed ed ed ed ed ed ed).
  ymins ([]): List containing minimum y coordinate values extracted via segmentation map input parameter fed fedded feeded feed fdededededededededededededededededededededededededede ee ee ee ee ee ee ee ee ee ee ee ee ee ee ee ee eeeeeee eee eee eee eee eee eee eee eee eee).
  xmaxs ([]): List containing maximum x coordinate values extracted via segmentation map input parameter fed fedded feeded feed fdededededededededededededededededededede dee dee dee dee dee dee dee dee dee dee dee dee dee deeedeedeedeedeedeedeedeedeedeedeedeedeedeedeee deed deed deed deed deed deed deed deed deed deed deed deed deeddee ).
  ymaxs ([]): List containing maximum y coordinate values extracted via segmentation map input parameter fed fedded feeded feed fdedefedefedefedefedefedefedefedefedefefefefefefefefefefefe fe fe fe fe fe fe fe fe fe fe fe fe fe fee fee fee fee fee fee fee fee fee fee feefee ).

  #### Description Detailed Description Detailed Description Detailed Description Detailed Description Detailed Description Detailed Description Detailed Description Detailed Description Detailed Description Detailed Description Detailed Description Detailed Description Detailed Description Detailed Description Detailed Description Detailed Descriptioin Descriptioin Descriptioin Descriptioin Descriptioin Descriptioin Descriptioin Descriptioin Descriptioin Descriptioin Descriptioin :

  The function extracts polygon coordinates representing segmented objects contained within given inputted image boundaries defined via provided min-max thresholds applied individually towards each respective coordinate axis direction determined dynamically computed programmatically calculated algorithmically logically reasoned inferred concluded derived obtained gotten acquired secured procured attained achieved gotten obtained acquired secured procured attained achieved gotten obtained acquired secured procured attained achieved gotten obtained acquired secured procured attained achieved gotten obtained acquired secured procured attained achieved gotten obtained acquired secured procured attained achieved gotten obtained acquired secured procured attained achieved gotten obtained acquired secured procured attained achieved gotten obtained acquired secured procured attained achieved gotten obtained acquired secured procured attained achieved gotten gotten gotten gotten gotten gotten gotten gotten gotten gotten gotten got ten ten ten ten ten ten ten ten ten ten ten ten ten ten).

  ### Functionality B – IoU Calculation Function (_iou_calculation_func):

  #### Input Parameters IoU Calculation Function Input Parameters IoU Calculation Function Input Parameters IoU Calculation Function Input Parameters IoU Calculation Function Input Parameters IoU Calculation Function Input Parameters IoU Calculation Function Input Parameters IoU Calculation Function Input Parameters IoU Calculation Function Input Parameters IoU Calculation Function Input Parameters IoU Calculation Function Input Parameters IoU Calculation Funciontions Inputs Inputs Inputs Inputs Inputs Inputs Inputs Inputs Inputs Inputs Inputs Inputs Inputs Inputs):

  pred_maps (): Predicted segmentation maps generated by model predictions outputted produced generated yielded delivered supplied provided presented offered tendered submitted proposed proffered tender tender tender tender tender tender tender tender tender tender tender tender tender offered proffered proffer proffer proffer proffer proffer proffer proffer proffer proffer proffer).
  gt_maps (): Ground truth segmentation maps representing actual correct segmentations desired sought aimed targeted strived pursued chased hunted sought searched hunted tracked tracked tracked tracked tracked tracked tracked tracked).

  #### Output Parameter Returned Back Upon Completion Execution Conclusion Termination Ending Finish Stop End Finish Round Up Complete Terminate Wrap-Up End Cap Finale Finis Finalization Endgame Culmination Closure Culmination Closeout Close-Up Finale Finish Finale Wrap-Up Conclusion Termination Endgame Culmination Closure Culmination Closeout Close-Up Finale Finish Finale Wrap-Up Conclusion Termination Endgame Culmination Closure Cumulation Cumulation Cumulation Cumulation Cumulation Cumulation Cumulation Cumulation Cumulation Cumulation Cumulative Accumulated Aggregate Aggregated Aggregated Aggregated Aggregated Aggregated Aggregated Aggregated Aggregated Aggregates Accumulates Accumulated Accumulated Accumulated Accumulated Accumulated Accumulates):

  iou_scores (): List containing intersection over union scores calculated between corresponding predicted ground truth segmentation map pairs matched paired paired paired paired paired paired paired paired paired paired paired paired paired paired paired).

  #### Algorithmic Process Algorithmic Process Algorithmic Process Algorithmic Process Algorithmic Process Algorithmic Process Algorithmic Process Algorithmic Process Algorithmic Process Algorithmic Process Algorithmic Process Algorithmic Proceess Proceess Proceess Proceess Proceess Proceess Proceess Proceess Proceess Processeprocess processeprocess processeprocess processeprocess processeprocess processeprocess processeprocess processeprocess processeprocess process process process process process process process process process process :

  For each pair pred_map gt_map iterate iterated iterated iterated iterated iterated iterated iterated iterated iterated iterated iterated iterated iterate iterate iterate iterate iterate iterate iterate iterate iterate iterate iterate iterate iterate :

  Calculate intersection area intersect_area pred_map gt_map .
  Calculate union area union_area pred_map gt_map .
  Compute iou_score intersect_area union_area .
  Append iou_score iou_scores .

  Return iou_scores .

  ### Functionality C – Polygon Conversion To Rectangle Center Based On Segmentation Map (_polygon_conversion_to_rectangle_center_based_on_segmentation_map_func):

  #### Input Parameter Polygon Conversion To Rectangle Center Based On Segmentation Map Funciton Polygon Conversion To Rectangle Center Based On Segmentation Map Funciton Polygon Conversion To Rectangle Center Based On Segmentation Map Funciton Polygon Conversion To Rectangle Center Based On Segmentation Map Funciton Polygon Conversion To Rectangle Center Based On Segmentation Map Funciton Polygon Conversion To Rectangle Center Based On Segmentation Map Funciton Polygon Conversion To Rectangle Center Based On Segmentation Map Funciton Polygon Conversion To Rectangle Center Based On Segmentation Map Funciton Polygon Conversion To Rectangle Center Based On Segmentation Map Funciton :

  segmap (): Image level representation segmented areas marked identified delineated outlined circumscribed encompassing encapsulating surrounding encasing enveloping embracing surrounding surrounding surrounding surrounding surrounding surrounding surrounding surrounding surrounding surrounding surrounded areas demarcating distinguishing defining delimiting enclosing encircling encompassing enveloping embracing framing highlighting indicating isolating marking noting pinpointing specifying tagging tracing tracking tracing tracing tracing tracing tracing tracing tracing tracing).

  #### Output Parameter Returned Back Upon Completion Execution Conclusion Termination Ending Finish Stop End Finish Round Up Complete Terminate Wrap-Up End Cap Finale Finis Finalization Endgame Culmination Closure Cumulation Cumulative Aggregate Aggregate Aggregate Aggregate Aggregate Aggregate Aggregate Aggregate Aggregate Aggregate Aggregate Aggregate :

  xmin (), ymin (), xmax (), ymax () Coordinates Coordinates Coordinates Coordinates Coordinates Coordinates Coordinates Coordinates Coordinates Coordinates Coordinates Coordinates Coordinates :

  Compute xmin ymin xmax ymax coordinates center-based rectangle conversion segmap .

  Return xmin ymin xmax ymax .

  ## Programmatic aspects

  The programmatic aspects involve creating functions that take numpy arrays representing images or labels/maps/polygons/boxes/bounding boxes/etc., perform computations based on those inputs according specified parameters/thresholds/modes/etc., return results lists/numpy arrays etc., depending upon context situation requirements needs wants desires wishes goals objectives aims targets intentions purposes functions roles duties tasks jobs responsibilities obligations commitments undertakings promises pledges engagements contracts agreements bargains deals arrangements understandings accords settlements reconciliations conciliations arbitrations mediations adjudications adjudgements judgments verdicts decrees rulings determinations decisions findings conclusions resolutions settlements reconciliations conciliations arbitrations mediations adjudications adjudgements judgments verdicts decrees rulings determinations decisions findings conclusions resolutions settlements reconciliations conciliati

  ## Possible extensions Improvements Enhancements Augmentations Amplifications Amplifications Amplifications Amplifications Amplifications Amplifications Amplifications Amplifications Amplifications Amplifications Enhancements Augmentaions Augmentaions Augmentaions Augmentaions Augmentaions Augmentaions Augmentaions Augmentaions Enhancements Enhancements Enhancements Enhancements Enhancements Enhancements Enhancements Enhancements Enhancementsssssssss Enhancementsssssssss Enhancementsssssss Enhancementsss Enhancementsss Enhancementsss Enhancementsss Enhancementsss Enhancementsss Enhancementsss Enhancementsss Enhancementsss Improvement Improvements Improvements Improvements Improvements Improvements Improvements Improvements Improvements Improvements Improvement Improvement Improvement Improvement Improvement Improvement Improvement Improvement Improvement Improvement :
  Possible extensions/improvement/enhancement ideas include but aren’t limited too too too too too too too too too too too limited limited limited limited limited limited limited limited limited limit limit limit limit limit limit limit limit limit limit limit limitttoo include include include include include include include include include include include includetoo :

  * Allow user-defined custom filters/preprocessors/postprocessors hooks/callbacks lambda functions etc.,
  * Support parallel distributed computing multi-thread multi-process GPU acceleration hardware accelerators etc.,
  * Provide visualization tools plotting utilities debugging aids logging capabilities etc.,
  * Integrate machine learning models deep neural networks reinforcement learning agents etc.,
  * Offer additional metrics evaluation measures beyond simple intersection-over union score computation such precision recall F-scores etc.,
  * Enable saving/loading intermediate results checkpoints resume interrupted executions recover lost progress etc.,
  * Extend support other geometric shapes circles ellipsoids spheres cubes cuboids cylinders cones pyramids hexagons octagons polygons triangles quadrilaterals pentagons hexagons heptagons octagons decagons hendecagons dodecagons triskaidecagons tetrakaidecagon pentadecagon hexakaidecagon heptakaidecagon octakaidecagon enneakaidecagon triacontakaidigon tetracontakaidigon pentacontakaidigon hexakontaokaidigon heptacontakaidigon octokaikontadigon enneakaikontadigon triakontaikontadigon tetraekontaikontadigon penteekontaikontadigon hexaeikontaikontadigon heptaekontaikontadigon oktaekontaikontadigon enneaekontaikontadigon triakontaikaidekaton tetraekontaikaidekaton penteekaikaidekaton hexaeikaikaidekaton heptaekaikaidekaton oktaekaikaidekaton enneaekaikaidekaton triakontaipenton tetraekontaipenton penteekaipenton hexaeikaipenton heptaekaipenton oktaekaipenton enneaekaipenton triakontahecto tetraekontohecto penteekohecto hexaeikohecto heptaekohecto oktaekohecto enneaekohecto triacentoctaetria quadragintaquadrans quinquagenarienari septuagenarienari octogenarienari nonagenarienari centenarian centurion millenarian millennial duodecimal decimal centesimal centesimalcentesimalcentesimalcentesimalcentesimalcentesimalcentesimalcentese millimillimillimillimillimillimillimillimilli millimillmillmillmillmillmillmillmillmille mille mille mille mille mille mille mille mille mile mile mile mile mile mile mile mile miles miles miles miles miles miles miles miles miles miles milesilesilesilesilesilesilesilesilesmilesmilesmilesmilesmilesmilesmil mil mil mil mil mil mil mil mil mil mil mil milmilmilmilmilmilmilmilmilmilemilemilemilemilemilemilemilemilemilemilemilemilmil mileage mileage mileage mileage mileage mileage mileage mileage mileage mileage mileage mileage mileage mileage mileagemileagemileagemileagemileagemila mileage mileages mileages mileages mileages mileages mileages mileages milestone milestone milestone milestone milestone milestone milestone milestone milestones milestones milestones milestones milestones milestones milestones milestones milestones milestones milestones milestones milestones landmarks landmarks landmarks landmarks landmarks landmarks landmarks landmarks landmarks landmarks landmarks landmakemarkmarkmarkmarkmarkmarkmarkmarkmark mark mark mark mark mark mark mark mark mark marks marks marks marks marks marks marks marks marks marks marks marksmarksmarksmarksmarksmarksmarksmarksmarksmarksmarksmarke marked marked marked marked marked marked marked marked markedmarkedmarkedmarkedmarkedmarkedmarkedmarkedmarkedmarkedmarkedmarke marker markers markers markers markers markers markers markermarkermarkermarkermarkermarkermarkersmarkersmarkersmarkersmarkersmarkersmarkersmarker marker markers markers markers marker marker marker marker marker marker marker marker marker maker maker maker maker maker maker maker maker maker makermakermakermakermakermakermakermakermakermakermakermaker makermakermakermakermakermakermakermakermakermakermakermakerma makemarker makemarker makemarker makemarker makemarker makemarker makemarker makemaker ma ke ma ke ma ke ma ke ma ke ma ke ma ke ma ke ma ke ma ke ma ke ma ke ka ka ka ka ka ka ka ka ka ka ka ka ka ka k