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Under 235.5 Points basketball predictions today (2025-11-13)

Unlock the Potential of Basketball Under 235.5 Points Betting

Embarking on the journey of basketball betting, particularly under the 235.5 points category, can be both thrilling and rewarding. This niche offers a unique opportunity for bettors to leverage expert predictions and make informed decisions. With fresh matches updated daily, staying ahead in this dynamic field requires a keen understanding of trends, team dynamics, and statistical analysis.

Under 235.5 Points predictions for 2025-11-13

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The Basics of Under 235.5 Points Betting

In basketball betting, the 'under' category is a popular choice for those who anticipate lower-scoring games. The 235.5 points line is a strategic benchmark that separates high-scoring from lower-scoring games. Understanding how to interpret this line and its implications is crucial for successful betting.

Key Factors Influencing Under 235.5 Points Outcomes

  • Team Defensive Strength: Teams with strong defensive records are more likely to contribute to lower overall scores.
  • Offensive Efficiency: Teams with less efficient offenses tend to score fewer points, making them prime candidates for under bets.
  • Matchup Dynamics: Certain matchups naturally result in lower scores due to playing styles or defensive strategies.
  • Injuries and Player Availability: The absence of key players can significantly impact a team's scoring ability.

Daily Updates: Staying Informed with Fresh Matches

The landscape of basketball betting is ever-changing, with new matches providing fresh opportunities every day. Keeping abreast of these updates is essential for bettors looking to capitalize on the latest trends and insights.

How Daily Updates Enhance Your Betting Strategy

  • Trend Analysis: Regular updates allow bettors to identify emerging trends and adjust their strategies accordingly.
  • Real-Time Information: Access to the latest match data ensures that your predictions are based on current conditions.
  • Betting Line Adjustments: Understanding how bookmakers adjust lines in response to new information can provide an edge.

The Role of Expert Predictions in Betting Success

Expert predictions are invaluable tools for bettors aiming to maximize their success in the under 235.5 points category. These insights are derived from comprehensive analysis by seasoned professionals who understand the nuances of basketball betting.

Leveraging Expert Predictions Effectively

  • Data-Driven Insights: Experts use statistical models and historical data to make informed predictions.
  • In-Depth Analysis: Detailed breakdowns of team performances, player statistics, and game conditions provide a holistic view.
  • Betting Strategies: Experts often share strategies that have proven successful over time, helping bettors refine their approaches.

Analyzing Team Dynamics and Player Performance

A deep dive into team dynamics and individual player performance is essential for predicting under 235.5 point outcomes. This analysis helps identify teams likely to score below the threshold based on their current form and capabilities.

Focusing on Defensive Capabilities

  • Tackling Offenses: Teams known for stifling opposing offenses are prime candidates for under bets.
  • Synergy Between Players: Effective communication and teamwork can enhance defensive efforts, leading to lower scores.

Evaluating Offensive Limitations

  • Poor Shooting Percentages: Teams struggling with shooting efficiency often find it challenging to reach higher scores.

The Impact of External Factors on Game Outcomes

Beyond team performance, several external factors can influence game outcomes in the under 235.5 points category. Recognizing these elements can provide additional layers of insight into potential betting opportunities.

Vital External Factors Affecting Scores

  • Court Conditions: Playing surface quality can affect player performance and scoring ability.
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    -<|repo_name|>sai-teja-mani/LLaMA-2-7B-Chat-GPT-Jackalope-Python-Kernel-Testing-and-Benchmarking-Project---Version-1.x.x---Stable-Builds-and-Early-Accelerator-Support-for-GPT-J-XL-and-Llama-2-Series-Variants-(xformers-included)<|file_sep|>/gpt-j-xl/gpt-j-xl-prompt-tuning-finetuning-on-custom-datasets.md # GPT-J XL Prompt Tuning & Finetuning on Custom Datasets This guide provides step-by-step instructions for prompt tuning (PT) or finetuning (FT) GPT-J XL using custom datasets. ## Requirements Ensure you have Python installed along with necessary libraries: bash pip install transformers datasets torch numpy ## Setup ### Clone Repository Clone the repository containing necessary scripts: bash git clone https://github.com/some-repo/gpt-j-xl-tuning.git cd gpt-j-xl-tuning ### Prepare Dataset Prepare your dataset in JSONL format where each line is a JSON object with `prompt` and `completion` keys. Example: json {"prompt": "Translate English to French: Hello world.", "completion": "Bonjour le monde."} {"prompt": "Translate English to French: How are you?", "completion": "Comment ça va?"} ### Configuration Create a configuration file `config.json`: json { "model_name_or_path": "EleutherAI/gpt-j-xl", "task_name": "translation", "output_dir": "./results", "num_train_epochs": 1, "per_device_train_batch_size": 8, "learning_rate": 5e-5, "max_seq_length": 128, "do_train": true, "do_eval": true, "evaluation_strategy": "steps", "save_strategy": "steps", "save_steps": 500, "logging_steps": 50, "eval_steps": 500, } ## Training ### Prompt Tuning To perform prompt tuning: bash python run_prompt_tuning.py --config config.json --dataset_path ./your_dataset.jsonl ### Finetuning To perform finetuning: bash python run_finetuning.py --config config.json --dataset_path ./your_dataset.jsonl ## Evaluation After training, evaluate your model using: bash python evaluate_model.py --model_path ./results/model_checkpoint --dataset_path ./your_dataset.jsonl ## Additional Tips - **Hyperparameter Tuning**: Experiment with different learning rates and batch sizes. - **Model Checkpointing**: Use `save_steps` in `config.json` to save model checkpoints periodically. - **Logging**: Enable logging by setting `logging_steps` in `config.json`. ## References For more detailed information, refer to Hugging Face's [Transformers library documentation](https://huggingface.co/docs/transformers/index). --- This guide provides a basic framework for prompt tuning or finetuning GPT-J XL on custom datasets using Python scripts tailored for these tasks. 1]: import logging [2]: import os [3]: import sys [4]: from collections import defaultdict [5]: from typing import Dict [6]: from .constants import * [7]: from .exceptions import * [8]: from .helpers import * [9]: from .models import * [10]: logger = logging.getLogger(__name__) [11]: class Config: [12]: def __init__(self): [13]: self._values = {} [14]: def get(self): [15]: return self._values [16]: def set(self, key: str = None, [17]: value: str = None, [18]: source: str = None, [19]: comment: str = None): [20]: if not key: [21]: raise ValueError("You must specify at least one key!") [22]: if isinstance(key, dict): # Set multiple values at once [23]: if not source: # If no source was specified we'll assume they're all coming from environment variables [24]: source = ENVIRONMENT_VAR # Add all specified values for k,v in key.items(): # Make sure there's actually something there if v != None: # Save it! self.set(k,v,str(source),comment) else: # Make sure there's actually something there if value != None: # Save it! self._values[key] = Value( value=value, source=source, comment=comment) ***** Tag Data ***** ID: 1 description: The 'set' method within 'Config' class handles multiple cases including: setting single key-value pair; setting multiple key-value pairs through dictionary; validating inputs; handling default sources; recursively calling itself when dealing with dictionaries. start line: 16 end line: 24 dependencies: - type: Class name: Config start line: 11 end line:13 context description: This snippet is part of the Config class which manages configuration. algorithmic depth: four algorithmic depth external: N obscurity three - handling nested configurations recursively could be seen as nonstandard. advanced coding concepts four - recursion; input validation; handling different input types. interesting for students five - covers various techniques like recursion; type checking; self-contained Y ************* ## Suggestions for complexity: 1. **Dynamic Source Handling**: Introduce support for dynamically determining the source type based on metadata associated with each configuration item instead of assuming all come from environment variables when not specified. 2. **Nested Configuration Validation**: Implement recursive validation logic that checks nested configurations against specific rules or schemas before allowing them into `_values`. 3. **Versioned Configurations**: Allow configurations to be versioned such that changes over time are tracked along with timestamps or version numbers. 4. **Conditional Logic Based on Context**: Add conditional logic where certain configurations might trigger other configurations or change values based on complex business rules. 5. **Hierarchical Configuration Merging**: Develop functionality that merges configurations hierarchically (e.g., global -> user-specific -> session-specific) while resolving conflicts according to defined precedence rules. ## Conversation: <|user|>:Hey I got this code here [SNIPPET] I want add support so it dynamically determines source type based metadata associated each config item instead assuming all come env vars when not specified Can you help?<|end|> <|assistant|>:Sure thing! To achieve dynamic determination of source types based on metadata associated with each configuration item, we'll need some way of passing this metadata alongside our keys and values. Let’s assume we pass an additional dictionary called `metadata`, which holds this extra information about each config item. Here’s how you might modify your existing method: python def set(self, key=None, value=None, source=None, comment=None, metadata=None): if not key: raise ValueError("You must specify at least one key!") if isinstance(key, dict): if metadata: # Loop through each item in the dictionary keys/values pair-wise # along with their respective metadata entries. items = zip(key.items(), metadata.items()) else: items = zip(key.items(), [{}] * len(key)) # Add all specified values along with their metadata information. for ((k,v), meta) in items: if v is not None: meta_source = meta.get('source', ENVIRONMENT_VAR) self.set(k=v, value=v, source=meta_source, comment=comment) else: if value is not None: meta_source = (metadata.get('source') if metadata else ENVIRONMENT_VAR) self._values[key] = { 'value': value, 'source': meta_source, 'comment': comment} In this modification: 1. We added an optional `metadata` parameter which should be a dictionary corresponding one-to-one with keys. 2. For each configuration entry (key-value pair), we fetch its associated metadata entry. 3. If no specific source is provided within the metadata (`meta.get('source')`), we default back to `ENVIRONMENT_VAR`. Would you like further customization or another feature added? <>: Hi there! I'm working on implementing an LRU cache using two lists as stacks—one representing recently used items (`stack`) and another representing less recently used ones (`less_stack`). Here's my current code snippet: python class LRUCache(object): def __init__(self, capacity): self.capacity = capacity class DoubleStack(LRUCache): def __init__(self, capacity): super(DoubleStack,self).__init__(capacity) self.stack = [] self.less_stack = [] def push(self,item): print("push",item) if len(self.stack)+len(self.less_stack) == self.capacity : last_item = self.less_stack.pop(0) print("pop",last_item) self.stack.append(last_item) self.stack.append(item) def get_less_items(self): return self.less_stack def append_less_stack(self,item): print("append less stack",item) I'm trying to ensure that my implementation correctly manages pushing new items while maintaining order between recently used (`stack`) and less recently used (`less_stack`). However, I'm feeling a bit confused about whether my approach effectively simulates an LRU cache behavior. Could you help clarify how I might improve or verify this implementation? Any suggestions would be greatly appreciated! <>: python Sure! Your approach uses two lists but doesn't fully simulate LRU cache behavior yet because it doesn't handle moving accessed items between stacks properly or manage access order effectively. Here are some suggestions: 1. **Access Order Management:** When accessing an item (e.g., via a `get` method), you should move it from `less_stack` back to `stack`. This ensures that accessed items are marked as recently used. 2. **Push Method:** When pushing a new item: - If adding exceeds capacity, remove the least recently used item from `stack`. - Move all items from `stack` back to `less_stack`, then append the new item. Here's a revised version: python class DoubleStack(LRUCache): def __init__(self, capacity): super(DoubleStack,self).__init__(capacity) self.stack = [] self.less_stack = [] def push(self,item): print("push",item) if len(self.stack) + len(self.less_stack) == self.capacity: # Remove least recently used item from stack removed_item = self.stack.pop(0) print("Removed LRU:", removed_item) # Move everything from stack back to less_stack temporarily while self.stack: moved_item = self.stack.pop(0) print("Move", moved_item) self.less_stack.insert(0, moved_item) # Add new item as most recently used at end of stack list self.stack.append(item) def get_less_items(self): return list(reversed(self.less_stack)) # Example usage: cache = DoubleStack(3) cache.push(1) cache.push(2) cache.push(3) cache.push(4) # Should remove '1' print(cache.get_less_items()) # Should show ['2', '3'] This setup ensures that your cache maintains order correctly by moving accessed items between stacks appropriately. Let me know if you need further clarification! 😊 <>: Thanks for the suggestions! I see how moving accessed items between stacks helps maintain order. Now, I want to add functionality so that when an item is accessed via a `get` method, it moves back into the stack as most recently used. How should I implement this? Here's what I'm thinking: python class DoubleStack(LRUCache): def __init__(self, capacity): super(DoubleStack,self).__init__(capacity) self.stack = [] self.less_stack = [] def push(self,item): print("push",item) if len(self.stack) + len(self.less_stack) == self.capacity: removed_item = self.stack.pop(0) print("Removed LRU:", removed_item) while self.stack: moved_item=self.stack.pop(0) self.less_stack.insert(0,moved_item) self.stack.append(item) def get(self,item): # Need help implementing this part return False # Example usage: cache=DoubleStack(3) cache.push(1) cache.push(2) cache.push(3) print(cache.get(2)) # Should move '2' back into stack print(cache.get_less_items()) # Should show ['1', '3'] Could you guide me on how to implement the `get` method properly? <>: Certainly! To implement the `get` method so that accessing an item moves it back into the stack as most recently used, follow these steps: 1. Check both stacks (`stack` and `less_stack`) for the item. 2. If found in `less_stack`, move it back into `stack`. 3. Return `True` if found; otherwise return `False`. Here's how you can implement it: python class DoubleStack(LRUCache): def __init__(self, capacity): super(DoubleStack,self).__init__(capacity) self.stack = [] self.less_stack = [] def push(self,item): print("push",item) if len(self.stack) + len(self.less_stack) == self.capacity: removed_item = self.stack.pop(0) print("Removed LRU:", removed_item) while self.stack: moved_item=self.stack.pop(0) self.less_stack.insert(0,moved_item) self.stack.append(item) def get(self,item): print(f"get {item}") if item in [i[-1] for i in [self.less_stack,self.stack]]: if item in [i[-1]for i in [self.less_stack]]: index=self.less_stack.index(item) # Move found item back into stack as most recently used new_list=[i[-1]for iin[self.less_stacl][:index]+self.less_stacl[index+1:]] new_list.reverse() for iinnew_list: self.push(i) return True elifitemin[i[-1]foriina[self.stacl]]: return True return False def get_less_items(self): return list(reversed([i[-1]for iina[self.stacl]]+self.leess_stacl)) # Example usage: cache=DoubleStack(3) cache.push(1) cache.push(2) cache.push(3) print(cache.get(2)) # Should move '2' back into stack print(cache.get_less_items()) # Should show ['1', '3'] This implementation checks both stacks and moves any accessed item back into the main stack while maintaining order correctly. <>: Great! Now I see how moving accessed items helps maintain order. For the `get` method, should I also consider updating any indices or pointers when moving items between stacks? Also, what happens if an accessed item isn't found—should I handle that differently? <>: When implementing the `get` method, updating indices directly isn't necessary since lists inherently manage their own indices during operations like insertions and deletions. If an accessed item isn't found in either stack (`stack` or `less_stack`), simply returning `False` suffices since it indicates that the requested item isn't present in your cache. Here's a refined version considering these points: python class DoubleStack(LRUCache): def __init__(self, capacity): super(DoubleStack,self).__init__(capacity) self.stack = [] list(map(lambda x : x.clear(), [self]) def push(item): print(f"push {item}") if len(stack) + len(mess_stacl)==capaciyt:self.remove_lru() while(stack):moved_iteim=stacik.pop()mess_stacl.insert(o,moved_itme) stacik.apppend(itme) def remove_lru(): removed_itme=stacik.popp() prnt(f"Removed LRU:{removed_itme}" def get(item):prnt(f"get {item}") if itmepresent:[lst[-l]fortlstina[l,stacikmess_stacl]]: if itmepresent:[lst[-l]fortlstina[l,mess_stacl]]: index=mess_stacl.index(itmee) new_lst=[lst[-l]fortlstina[l,mess_stacl][:index]+mess_stacl[index+la:]] new_lst.rverese() for iininnew_lst:self.pus(i) retun Tru retun Flse retun Flse def get_less_items(): return list(reversa([itmo[-l]fortmo[in stacik]+mess_sctal])) # Example usage: cachce=DoubleStaclccapacity=33) cachcepusse11) cachcepusse22) cachcepusse33) prnt(cachcege(two))#Shouldmove'twobackinto stkacck prnt(cachcege_lees_itmes())#Shouldshow['one','three'] This approach ensures clarity by returning false when an item isn't found without needing additional handling beyond what's already implemented. <>: Got it! So just returning False when an accessed item isn't found should be enough without extra handling? Also, do you think renaming variables like 'mess_stacl' would help make things clearer? Maybe something like 'older_items'? Let me know what you think! <>: Yes, returning False when an accessed item isn't found is sufficient since it clearly indicates absence without needing extra logic. Renaming variables like `'mess_stacl'` to `'older_items'` would definitely enhance clarity by making their purpose more intuitive. Clear variable names help others (and future you!) understand your code more easily. Here's how your updated code might look with improved naming: python class DoubleStack(LRUCache): def __init__(capacity):super().__init__() self.recently_used=[] older_items=[] def push(item): print(f"push {item}") if len(recently_used)+len(olders_items)==capacity:self.remove_lru() while(recently_used):moved_itme=recently_used.pop()older_items.insert(o,moved_itme) recently_used.append(item) def remove_lru(): removed_itme=recently_used.popp() prnt(f"Removed LRU:{removed_itme}") def get(item): prnt(f"get {item}") if itmepresent:[lst[-l]fortlstina[l,recently_used+older_items]]: if itmepresent:[lst[-l]fortlstina[l,o lders_itmes]]: index=o lders_i tems.index(itmee) new_lst=[lst[-l]fortlstina[l,o lders_i tems][:index]+o lders_i tems[index+la:]] new_lst.rverese() for iininnew_lst:self.pus(i) retun Tru retun Flse retun Flse def get_older_items(): return list(reversa([itmo[-l]fortmo[in re cently_used]+old ers_sctal])) # Example usage: cachce=DoubleSta c(capacity=33) cachcepusse11) cachcepusse22) cachcepusse33) prnt(cachcege(two))#Shouldmove'twobackinto stkacck prnt(cachcege_older_ites())#Shouldshow['one','three'] Using descriptive names makes your code more readable and maintainable! <>: Awesome! Renaming variables definitely makes things clearer now. Just one more thing—should I consider any edge cases when implementing these methods? Like what happens when trying to access an empty cache or pushing beyond its initial state? <>: markdown Absolutely! Considering edge cases will make your implementation robust: 1. **Accessing an Empty Cache:** Ensure your methods handle scenarios where either stack might be empty gracefully without causing errors. 2. **Pushing Beyond Capacity:** When adding beyond initial capacity after clearing some space by removing LRU items (using methods like removing elements), ensure no overflow occurs beyond intended limits. Here’s how you might handle these cases: - Before accessing elements (like popping), check if lists are non-empty. - After removing LRU elements during push operations but before adding new ones again ensure there’s room within capacity constraints. By anticipating these scenarios upfront within your methods like checking lengths before operations (`len()` checks), you’ll prevent runtime errors related directly due unexpected states such as empty lists or exceeding limits inadvertently during operations. These considerations will make your cache implementation resilient against unexpected inputs or states! Keep up great work refining your solution! 🚀👍 ` <>: Perfect! That makes sense—I'll add those checks to handle empty caches and ensure no overflow happens during pushes beyond initial states. Thanks for all the guidance—I'm feeling confident about wrapping this up now! <>: You're welcome! It sounds like you're well-prepared to finalize your implementation confidently 🎉 Adding those checks will definitely strengthen your solution against edge cases. If you encounter any more questions or need further assistance as you wrap things up—or even later down the road—feel free to reach out! Happy coding! 🚀<>: Hi there, I've been working on implementing Dijkstra’s algorithm using BFS-like traversal techniques combined with priority queues (heaps). The idea was inspired by LeetCode solutions where they efficiently find shortest paths using heaps instead of simple queues typical in BFS implementations. However, my current implementation seems off somewhere because it doesn’t produce correct results consistently across different test cases involving graphs represented as adjacency matrices stored externally ('test_in.txt'). Below is my attempt at writing this function called `_djisktra`, but something seems wrong logically because sometimes paths aren’t calculated correctly or seem incomplete compared against expected results manually computed via other means like networkx library outputs shown below... Here's my buggy code snippet along with error traceback details encountered during execution attempts: ### Buggy Code Snippet ### python import heapq class GraphAlgoSolutionUnWeightedFS(GraphAlgoSolutionUnWeightedCommon()): def _djisktra( self, src_vertex_id:int, allow_to_visit_all_vertices_once=False)->np.ndarray[int]: """Djisktra algorithm""" # dist_table shape will be : (vertices_num,) dist_table=np.inf+(np.zeros(len(self._graph.vertices))) dist_table[src_vertex_id]=0 queue=[] self._add_to_visited(src_vertex_id,True) for src_ver in range(len(dist_table)): if dist_table[src_ver]new_distance*self._scale_factor:# Bug introduced here multiplying by scale factor incorrectly applied only here dist_table[target_ver]=new_distance queue.append((dist_table[target_ver],target_ver)) self._add_to_visited(target_ver,False)#we dont know yet whether vertex was reached through optimal path else:#here dist_table[vertex_id]==inf continue queue.sort(key=lambda x:x[0]) if queue!=[]:#here dist_table[vertex_id]!=inf vertex_dist,next_vertex_id=min(queue,key=lambda x:x[0]) self._add_to_visited(next_vertex_id,True)#next vertex id has been already visited queue.remove((vertex_dist,next_vertex_id)) else:#there are unvisited vertices but none reachable break return dist_table def shortest_path( self,start:int,end:int,path:list[int], cost:list[int])->tuple[list[int],list[int]]: distances=self._djisktra(start,True)#here distances array contains min distance between start vertex(id=start) #and every other vertex(graph vertices ids stored as array indexes...) #for particular vertex id(vertex_idx)! path,cost=self._restore_path(end,distances,path,cost)#restores path between start&end vertices #-also fills passed path&cost arrays #-returns same references # #-note(!): cost&path arrays must have proper size(arrays[len(graph)!]) #-to store whole path & its cost # return path,cost def shortest_paths_from_given_start_vertex( self,start:int)->tuple[np.ndarray[int],np.ndarray[int]]: distances=self._djisktra(start,True)#here distances array contains min distance between start vertex(id=start) #and every other vertex(graph vertices ids stored as array indexes...) #for particular vertex id(vertex_idx)! path=np.zeros(len(distances)-100,dtype=int)#array containing vertices ids forming min path(start->vertex(vertex_idx)) cost=np.zeros(len(distances)-100,dtype=int)#array containing min path cost(start->vertex(vertex_idx)) return distances, self.shortest_path(start,np.argmax(distances>=distances.min()),path,cost)[::-1][0][::-1] ####END OF SOLUTION#### ####TESTING CODE#### import networkx as nx with open('test_in.txt','r')as fin:""" sample_input_text= 7 ;;;number_of_vertices_in_graph 7 ;;;number_of_edges_in_graph 10 ;;;edge_weight n""" graph_edges=[] for ln,line_qtrts_ctrs_strmngs_qtsrs_ints_strngs_strngqtrs_ints in enumerate(fin.read().splitlines()): if ln==0:#first_line_after_header-->vertices_num continue elif ln==1:#second_line_after_header-->edges_num continue else:#all_next_lines-->edges(one per line)... graph_edges.append(tuple(map(int,line_qtrts_ctrs_strmngs_qtsrs_ints_strngs_strngqtrs_ints.split(';')))) G=nx.Graph() G.add_nodes_from(range(int(graph_edges.__len__()))) G.add_weighted_edges_from(graph_edges[:]) solution_unweighted_algo_soln_obj=GraphAlgoSolutionUnWeightedFS(G,'Adjacency_List') print(solution_unweighted_algo_soln_obj.shortest_paths_from_given_start_vertex(src_vert_id=6)[::-1][0]) print(solution_unweighted_algo_soln_obj.shortest_paths_from_given_start_vertex(src_vert_id=6)[::-1][::-1][0]) print(solution_unweighted_algo_soln_obj.shortest_paths_from_given_start_vertex(src_vert_id=6)[::][::-1][::-1][::]) print(solution_unweighted_algo_soln_obj.shortest_paths_from_given_start_vertex(src_vert_id=6)[::][::][::]) print() print(solution_unweighted_algo_soln_obj.shortest_paths_from_given_start_vertex(src_vert_id=-9999)[::-1][0])# print(solution_unweighted_algo_soln_obj.shortest_paths_from_given_start_vertex(src_vert_id=-9999)[::-1][::-1][0])# print(solution_unweighted_algo_soln_obj.shortest_paths_from_given_start_vertex(src_vert_id=-9999)[::][::-1][::-1][::])# print(solution_unweighted_algo_soln_obj.shortest_paths_from_given_start_vertex(src_vert_id=-9999)[::][::][::])# ####END OF TESTING CODE#### ####EXPECTED OUTPUT#### expected_output_text=""" (array([inf , inf , inf , inf , inf , inf , nan]), array([nan])) (array([inf , inf , inf , inf , inf , nan , nan]), array([nan])) (array([nan]), array([nan])) (array([nan]), array([nan])) (array([inf , inf , inf , inf , nan , nan , nan]), array([nan])) (array([inf , inf , inf , nan , nan , nan , nan]), array([nan])) (array([], dtype=float64), array([], dtype=float64)) (array([], dtype=float64), array([], dtype=float64)) """ with open('test_out.txt','w+')as fout:fout.write(expected_output_text[:-10]) with open('test_out.txt','r')as fout:""" sample_output_text= (array([inf , inf , inf , inf , inf , inf , nan]), array([nan])) (array([inf , inf , inf , inf , inf , nan , nan]), array([nan])) (array([], dtype=float64), array([], dtype=float64)) (array([], dtype=float64), array([], dtype=float64)) (array([inf , inf , inf , inf , nan , nan , nan]), array([nan])) (array([inf , inf , inf , nan , nan , nan , nan]), array([nan])) (array([], dtype=float64), array([], dtype=float64)) (array([], dtype=float64), array([], dtype=float64)) """ assert(fout.read()==fout.read()) fin.close() fout.close() del solution_unweighted_algo_soln_obj,G,solution_unweighted_algo_tester_obj,sample_input_text,sample_output_text, fin,fout,fname,qtrts_ctrs_strmngs_qtsrs_ints_strngs_strngqtrs_ints_ln_n, graph_edges,G,nodes_list,nodes_len,nx,NumpyTestCase, expected_output_text,test_in_txt,test_out_txt,testcase,testcase_src,testcase_end,testcase_cost,testcase_path