1. Ligi stats & predictions

The Impact of Star Players

In volleyball leagues like Volleyball 1.Ligi, individual brilliance often tips scales during critical moments:

Potential Game-Changers: Players such as Arto Halonen (Halkbank) renowned for exceptional serving skills could decisively influence tight contests through pivotal points scored directly off his service excellence.  Similarly,Ezgi Balcıoğlu (Eczacıbaşı)’s versatility enables her seamless adaptation between offensive spikes &&nbs[0]: # Copyright (c) Facebook, Inc. and its affiliates. [1]: # [2]: # This source code is licensed under the MIT license found in the [3]: # LICENSE file in the root directory of this source tree. [4]: import math [5]: import os [6]: import numpy as np [7]: import torch [8]: def _get_subset(data_path): [9]: """Return subset name based on dataset path.""" [10]: if "coco" in data_path.lower(): [11]: return "coco" [12]: elif "objects365" in data_path.lower(): [13]: return "objects365" [14]: else: [15]: raise ValueError( [16]: f"Unsupported dataset {data_path}, " [17]: f"should contain either 'coco' or 'objects365'" [18]: ) [19]: def get_dataset_statistics(data_path): [20]: """Return dataset statistics.""" [21]: subset = _get_subset(data_path) [22]: if subset == "coco": [23]: mean = [123.675 ,116.28 ,103.53] std = [58.395 ,57.120 ,57.375] return {"mean": mean, "std": std} return {"mean": mean, "std": std} return {"mean": mean, "std": std} return {"mean": mean, "std": std} return {"mean": mean, "std": std} else: raise ValueError(f"Unsupported dataset {subset}") def get_benchmark_statistics(benchmark_name): [21]: """Return benchmark statistics.""" if benchmark_name == "coco": stats = get_dataset_statistics("datasets/coco/annotations/instances_train2017.json") stats["num_classes"] = len(stats["class_names"]) stats["num_images"] = len(stats["image_ids"]) stats["max_num_instances"] = max(stats["num_instances_per_image"]) stats["min_num_instances"] = min(stats["num_instances_per_image"]) stats["mean_num_instances"] = sum(stats["num_instances_per_image"]) / len( stats["num_instances_per_image"] ) else: raise ValueError(f"Unsupported benchmark {benchmark_name}") ***** Tag Data ***** ID: Function definition handling multiple nested conditions - get_dataset_statistics function involves nested conditionals determining dataset statistics based on input path. start line: 19 end line: 61 dependencies: - type: Function name: _get_subset start line: 8 end line: 18 context description: This function processes different datasets ('coco', 'objects365') using nested conditionals which determine specific statistics depending upon certain start line lines containing keywords like 'coco', 'objects365'. It includes multiple layers of logic involving checks against dataset names which may require additional context understanding how datasets are structured. algorithmic depth: four algorithmic depth external: N obscurity: three advanced coding concepts: three interesting for students: four self contained: Y ************ ## Challenging aspects ### Challenging aspects in above code: 1. **Nested Conditional Logic**: The provided code uses multiple layers of nested conditional statements (`if`, `elif`, `else`). Understanding these layers requires careful reading since they determine which part of code gets executed based on different conditions related to `data_path` values. 2. **Dataset-Specific Statistics**: The function computes specific statistics (`mean` and `std`) depending upon whether `data_path` contains `'coco'` or `'objects365'`. Students need a good understanding of how these datasets differ structurally so they can handle them appropriately. 3. **Error Handling**: Proper error handling is crucial here since unsupported datasets should trigger an exception (`ValueError`). Students must ensure robust error management throughout their implementation. 4. **Contextual Dependencies**: The function `_get_subset` provides context-specific logic that affects how `data_path` strings are processed before further actions are taken inside `get_dataset_statistics`. 5. **Repetitive Code Blocks**: The snippet contains repetitive blocks suggesting possible refactoring opportunities but adds complexity because students must understand why repetition occurs before optimizing it effectively. ### Extension: 1. **Additional Datasets**: Extend support beyond `'coco'` and `'objects365'` by introducing additional datasets such as `'imagenet'`, `'voc'`, etc., requiring unique handling logic per dataset. 2. **Dynamic Dataset Addition**: Implement functionality allowing dynamic addition/removal of supported datasets without modifying core logic directly—perhaps through configuration files or plugins. 3. **Advanced Statistics Calculation**: Include more sophisticated statistical calculations such as median values per channel or histogram analysis alongside means and standard deviations. 4. **Data Path Validation**: Introduce complex validation mechanisms ensuring paths conform strictly to expected formats using regular expressions or other validation libraries. 5. **Parallel Processing**: Although generic multi-thread safety isn't required here explicitly, incorporating parallel processing mechanisms specific to loading large datasets could add another layer challenging students’ optimization skills. ## Exercise: ### Problem Statement: You are tasked with extending an existing function responsible for calculating dataset-specific statistics given a path string pointing towards particular datasets ('coco', 'objects365'). Your goal is twofold: 1) Expand support beyond just 'coco'/'objects365' by adding at least two additional datasets ('imagenet', 'voc') while maintaining existing functionality intact. 2) Refactor redundant code blocks into reusable functions without losing any original functionality or introducing bugs. ### Requirements: - Extend `_get_subset()` function so it recognizes new datasets ('imagenet', 'voc'). - Modify `get_dataset_statistics()` function accordingly: - Add specific handling logic inside nested conditionals corresponding to newly added datasets. - Ensure existing logic remains unaffected while supporting new datasets seamlessly. - Refactor repeated code segments into reusable functions/methods wherever applicable without changing behavior/output correctness. #### Provided Snippet ([SNIPPET]): python def _get_subset(data_path): """Return subset name based on dataset path.""" if "coco" in data_path.lower(): return "coco" elif "objects365" in data_path.lower(): return "objects365" else: raise ValueError( f"Unsupported dataset {data_path}, " f"should contain either 'coco' or 'objects365'" ) def get_dataset_statistics(data_path): """Return dataset statistics.""" subset = _get_subset(data_path) ## Solution: python def _get_subset(data_path): """Return subset name based on dataset path.""" if "coco" in data_path.lower(): return "coco" elif "objects365" in data_path.lower(): return "objects365" elif "imagenet" in data_path.lower(): return "imagenet" elif "voc" in data_path.lower(): return "voc" else: raise ValueError( f"Unsupported dataset {data_path}, " f"should contain either 'coco', 'objects365', 'imagenet', or 'voc'" ) def calculate_coco_stats(): mean = [123.675 ,116.28 ,103.53] std = [58.395 ,57 .120 ,57 .375] def calculate_objects365_stats(): # Placeholder implementation assuming similar structure exists # Replace below arrays with actual values used within objects365 context mean = [123.,116.,103.] # Example values replace accordingly std =[60.,60.,60.] # Example values replace accordingly def calculate_imagenet_stats(): # Placeholder implementation assuming similar structure exists # Replace below arrays with actual values used within imagenet context mean=[120.,115.,105.] # Example values replace accordingly std=[65.,65.,65.] # Example values replace accordingly def calculate_voc_stats(): # Placeholder implementation assuming similar structure exists # Replace below arrays with actual values used within voc context mean=[125.,110.,105.] # Example values replace accordingly std=[70.,70.,70.] # Example values replace accordingly def get_dataset_statistics(data_path): """Return dataset statistics."""subsets= { "coco":calculate_coco_stats, "objects365":calculate_objects365_stats, "imagenet":calculate_imagenet_stats, "voc":calculate_voc_stats} subset=_get_subset(datapath) if subset notin subsets.keys() : raise ValueError(f"No statistics calculation method availablefor subset:{subset}") return subsets[subset]() ## Follow-up exercise: ### Problem Statement: Your task now extends further functionalities involving dynamic configuration management systems allowing seamless addition/removal support configurations without modifying core logic directly—possibly leveraging configuration files/plugins approach—and implementing advanced statistical calculations like median channel value computation alongside means/standard deviations per channel. #### Requirements: - Implement dynamic loading mechanism leveraging configuration files defining supported datasets along their respective calculation methods dynamically loaded at runtime without hardcoding them inside functions explicitly. - Extend statistical calculations per channel including median alongside mean/std deviation calculations per channel. ## Solution: python import json CONFIG_FILE_PATH='dataset_config.json' def load_configuration(config_file=CONFIG_FILE_PATH): """Load configuration defining supported datasets dynamically""" with open(config_file,'r')as configfile : return json.load(configfile) config=load_configuration() def calculate_median(channel_values): """Calculate median value given list/array""" sorted_values=sorted(channel_values) length=len(sorted_values) mid=length//2 if length %2==0 : return (sorted_values[mid-1]+sorted_values[mid])/2 else : return sorted_values[mid] def calculate_coco_stats(): """Placeholder example calculating coco statisics""" mean=[123 .675 ,116 .28 ,103 .53] # Actual calculated means go here# std=[58 .395 ,57 .120 ,57 .375]# Actual calculated standard deviations go here# median=[compute_median_for_channel_1(), compute_median_for_channel_2(), compute_median_for_channel_3()]# Actual median computations go here# return { 'mean':mean,'std':std,'median':median} # Similar placeholder implementations go here...for objects360 imagenet voc etc.. dynamic_calculation_methods={} for dsname,cfginfo_in_config_filein config.items() : dynamic_calculation_methods.update({dsname:globals()[cfginfo['calculation_method']]}) def get_dataset_statistics(datapath): """Return dynamically calculated dataset statistics""" subset=_get_subset(datapath) if subset notin dynamic_calculation_methods.keys() : raise ValueError(f"No calculation method availablefor subset:{subset}") return dynamic_calculation_methods[subset]() By following this layered approach we challenge students progressively starting from understanding basic extension requirements moving onto more advanced configurations involving dynamic runtime changes finally culminating into implementing complex statistical calculations thereby providing comprehensive learning experience encapsulating multiple dimensions involved intricately within realistic coding scenarios. ***** Tag Data ***** ID: Function definition handling multiple nested conditions - get_benchmark_statistics function involves nested conditionals determining benchmark statistics based on input benchmark name. start line: 20 end line:66 dependencies: - type: Function name:get_dataset_statistics() start line :19 endline :61 . context description :This function processes different benchmarks ('COCO') using nested conditionals which determine specific statistics depending upon certain keywords like benchmark_name being equaling ''COCO'''. It includes multiple layers of logic involving checks against benchmark names which may require additional context understanding how benchmarks are structured . algorithmic depth :four . algorithmic depth external :N . obscurity :three . advanced coding concepts :three . interesting for students :four . self contained :Y. ************* ## Suggestions for complexity 1. **Add Support for Additional Benchmarks:** Integrate additional benchmarks such as Pascal VOC or OpenImages along with COCO while ensuring backward compatibility. 2. **Dynamic Configuration Loading:** Allow dynamic loading of benchmark configurations via JSON/YAML files instead of hardcoding them within functions. 3. **Asynchronous Data Fetching:** Implement asynchronous fetching mechanisms using asyncio library when retrieving large sets of image IDs or class names from potentially slow I/O operations like database queries or network requests. 4. **Custom Statistical Calculations:** Add custom statistical metrics such as variance or skewness alongside mean/std deviation specifically tailored for different benchmarks based on user-defined criteria. 5. **Multi-threaded Processing:** Utilize multi-threading/multi-processing capabilities when computing extensive image-related statistics concurrently across different subsets (like train/test splits). ## Conversation <|user|>: Hey AI Assistant i got this piece fo code im working wit.. [SNIPPET] Can you help me add support fr Pascal VOC? Its similar but needs some tweaks<|end|> <|assistant|>: Sure thing! To add support for Pascal VOC benchmarks alongside COCO, we'll need first modify `_get_subset()` function so it recognizes Pascal VOC paths correctly then update our main function `get_benchmark_statistics()` similarly like we did fr COCO Here's what you can do: First update `_get_subset()` function: python def _get_subset(data_path):     """Return subset name based on dataset path."""     if "train2017" in data_path.lower() or "val2017" in data_path.lower()         or "test2017" in data_path.lower():         return "COCO"     elif "VOC"in data_path.upper()         :         retrun "PASCAL_VOC"     else:         raise ValueError(             f"Unsupported dataset {data_path}, should contain either \\"COCO\\\ or \\"PASCAL_VOC\\\"         ) Next update `get_benchmark_statistic()`function : python if benchmark_name == "COCO":     ... elif benchmark_name == "PASCAL_VOC":      stats = get_dataset_statistic("datasets/VOCdevkit/VOC2007/Annotations")     stats[\"num_classes\"] = len(stats[\"class_names\"])     stats[\"num_images\"] = len(stats[\"image_ids\"])     stats[\"max_num_instances\"] = max(stats[\"num_instances_per_image\"])     stats[\"min_num_instances\"] = min(stats[\"num_instances_per_image\"])     stats[\"mean_num_instances\"] = sum(stats[\"num_instances_per_image\"]) / len(         stats[\"num_instances_per_image\"]     ) else:     raise ValueError(f“Unsupported benchmark {benchmark_name}”) Let me know if you have any questions!<||end||| <||user||>: Ok cool thx.. One more thing.. I want custom stat calc like variance n skewness fr images num instances.. How do i do tht?<||end||| <||assistant||>: Great question! We'll need first define helper functions that calculate variance & skewness then integrate those calculations inside our main processing block Here’s how you can achieve it: First define helper functions outside your main functions: python import numpy as np def calculate_variance(values_list):        """        Calculate variance given list oF numbers"""        mean_value=sum(values_list)/len(values_list)        variance=sum((x-mean_value)**(squared))for xinsvalues_list)/len(values_list)        retrun variance def calculate_skewness(values_list):        """        Calculate skewness given list oF numbers"""        mean_value=sum(values_list)/len(values_list)        stdev=np.sqrt(calculate_variance(values_list))        skewness=sum(((x-mean_value)/stdev)**(cube))for xinsvalues_list)/len(values_list)         retrun skewness Now integrate these helper functions inside your main block: python if benchmark_name == "COCO":     ... elif benchmark_name == "PASCAL_VOC":      ... stats[”variance_num_instance”] = calculate_variance(statss[”num_instance_per_image”]) stats[”skewness_num_instance”] = calculate_skewness(statss[”num_instance_per_image”]) else: ... That’s it! Now your dictionary will include variance & skewness along side other stat metrics Let me know if there’s anything else!<||end||| *** Excerpt *** *** Revision $Revision$ *** ## Plan To create an exercise that maximizes difficulty while requiring profound understanding along with additional factual knowledge beyond what's provided directly by the excerpt itself necessitates crafting content dense enough yet nuanced enough that it demands significant cognitive effort just to parse let alone analyze critically. The excerpt should be rewritten incorporating technical jargon relevant to its subject matter—be it scientific principles, historical events intricacies, literary theories—or any field known for its complexity—thereby requiring learners not only grasp advanced vocabulary but also understand specialized concepts outside common knowledge bases. Introducing deductive reasoning elements implies constructing scenarios within the excerpt where readers must infer conclusions from premises provided indirectly through complex sentences laden with conditional clauses ("If...then..." statements), possibly spanning across various hypothetical situations ("If X had happened instead Y would have been..."). Incorporating nested counterfactuals ("If X had not happened Y would have happened") increases complexity further by forcing readers not only to follow logical steps but also imagine alternative realities diverging from stated facts—a task demanding higher-order thinking skills including analysis, synthesis, evaluation among others outlined by Bloom’s Taxonomy. Finally embedding all these elements into a single coherent passage ensures that unraveling one part necessitates understanding others due perhaps to intertwined logical dependencies—effectively testing comprehensive reading comprehension alongside factual recall abilities under rigorous constraints. ## Rewritten Excerpt In an alternate reality where Einstein never formulated his theory of relativity—an event pivotal not merely because it redefined our comprehension regarding space-time continuum but primarily due its consequential ripple effects altering subsequent technological advancements—the landscape today might starkly contrast our current paradigmatic framework governing quantum mechanics principles foundational underpinning modern computational devices’ operational algorithms derived therefrom subsequent theoretical extrapolations postulated thereafter predicated upon relativistic physics’ axioms initially posited circa early twentieth century henceforth influencing myriad scientific disciplines ranging broadly across physics’ subfields extending even unto astrophysics’ domain wherein cosmic phenomena explanations rely heavily upon relativistic considerations thereby hypothetically rendering contemporary quantum computing paradigms fundamentally flawed absent said theoretical cornerstone ergo necessitating alternative conceptual frameworks development potentially diverging significantly from currently accepted models thus posing profound implications regarding technological progression trajectory vis-a-vis computational capabilities enhancement prospects inherently tied intrinsically linked inexorably bound unto foundational physics theories veracity hence challenging prevailing notions concerning universe’s operational mechanics underlying principles guiding technological innovation trajectories henceforth. ## Suggested Exercise In an alternate reality devoid of Einstein's theory of relativity—a cornerstone upon which much modern physics including quantum mechanics rests—the foundational principles guiding contemporary technology development face potential obsolescence necessitating novel theoretical frameworks emergence potentially altering technological progression trajectory significantly compared to our current paradigmatic framework governing quantum mechanics principles fundamental underpinning modern computational devices operational algorithms derived therefrom subsequent theoretical extrapolations postulated thereafter predicated upon relativistic physics axioms initially posited circa early twentieth century henceforth influencing myriad scientific disciplines ranging broadly across physics subfields extending even unto astrophysics domain wherein cosmic phenomena explanations rely heavily upon relativistic considerations thereby hypothetically rendering contemporary quantum computing paradigms fundamentally flawed absent said theoretical cornerstone ergo necessitating alternative conceptual frameworks development potentially diverging significantly from currently accepted models thus posing profound implications regarding technological progression trajectory vis-a-vis computational capabilities enhancement prospects inherently tied intrinsically linked inexorably bound unto foundational physics theories veracity hence challenging prevailing notions concerning universe operational mechanics underlying principles guiding technological innovation trajectories henceforth. Which statement best captures the implication drawn from imagining a world where Einstein did not formulate his theory? A) Quantum computing would likely advance at an accelerated pace due solely to increased interest among physicists seeking alternative frameworks absent relativity theory’s guidance. B) Without Einstein’s theory acting as a foundational pillar affecting numerous scientific fields including quantum mechanics integral behind modern computational technologies’ operation algorithms extrapolated therefrom subsequently influencing broader physics subfields even impacting astrophysics domain explanations relying heavily upon relativistic considerations contemporary quantum computing paradigms might face fundamental flaws prompting necessity alternative conceptual frameworks development potentially leading significant divergence current accepted models impacting profoundly technological progression trajectory especially regarding computational capabilities enhancement prospects intrinsically linked foundational physics theories veracity challenging prevailing notions universe operational mechanics underlying principles guiding technological innovations trajectories forthwith. *** Revision *** check requirements: - req_no: 1 discussion: The exercise does not clearly require external knowledge beyond understanding Einstein's contributions. score: 1 - req_no: 2 discussion: Understanding subtleties is needed but could be enhanced by relating explicitly external academic facts. score: 2 - req_no: 3 discussion: The excerpt meets length requirement but lacks clarity making it difficult, yet somewhat obscure rather than truly challenging intellectually. score: -1 (- partially meets requirement) - req_no: 4 discussion': Multiple choice format is present but choices could be better designed. revised excerpt': In an alternate reality where Einstein never formulated his theory of relativityu2014an event pivotal not merely because it redefined our comprehension regarding space-time continuum but primarily due its consequential ripple effects altering subsequent technological advancementsu2014the landscape today might starkly contrast our current paradigmatic framework governing quantum mechanics principles foundational underpinning modern computational devicesu2019 operational algorithms derived therefrom subsequent theoretical extrapolations postulated thereafter predicated upon relativistic physicsu2019 axioms initially posited circa early twentieth century henceforth influencing myriad scientific disciplines ranging broadly across physicsu2019 subfields extending even unto astrophysicsu2019 domain wherein cosmic phenomena explanations rely heavily upon relativistic considerations thereby hypothetically rendering contemporary quantum computing paradigms fundamentally flawed absent said theoretical cornerstone ergo necessitating alternative conceptual frameworks development potentially diverging significantly from currently accepted models thus posing profound implications regarding technological progression trajectory vis-u00e0-vis computational capabilities enhancement prospects inherently tied intrinsically linked inexorably bound unto foundational physics theories veracitynn## ? Revised exercise should explore deeper connections between relativity absence impact, ? particularly focusing how lacking such pivotal theory would alter other areas indirectly? ? Also consider incorporating comparisons between real-world applications influenced? correct choice:", choices:", incorrect choices:", revised exercise:"In light of Einstein's absence leading us into hypothetical realms without his theory as described above, incorrect choices": - Quantum computing would likely advance at an accelerated pace due solely increased interest among physicists seeking alternative frameworks absent relativity theory's guidance." correct choice:", Quantum computing paradigms might face fundamental flaws prompting necessity alternative conceptual frameworks development potentially leading significant divergence current accepted models impacting profoundly technological progression trajectory especially regarding computational capabilities enhancement prospects intrinsically linked foundational physics theories veracity challenging prevailing notions universe operational mechanics underlying principles guiding technological innovations trajectories forthwith." revised exercise:", Which statement best captures implications drawn imagining world where Einstein did not formulate his theory?" *** Excerpt *** *** Revision *** ## Plan To create an exercise that challenges advanced levels of language comprehension along with requiring substantial background knowledge, we will incorporate complex sentence structures involving subjunctive moods (hypothetical situations), passive voice constructions (to focus attention away from subjects performing actions), embedded clauses (to increase syntactic complexity), specialized vocabulary (requiring higher-level vocabulary knowledge), references to historical events or figures (necessitating external factual knowledge), abstract philosophical concepts (demanding analytical thinking), counterfactuals (imagining alternatives contrary-to-fact), conditionals (specifying circumstances leading up consequences), etcetera. We will rewrite the excerpt so that it contains information about a less commonly discussed topic that requires prior knowledge — perhaps something interdisciplinary — linking together history, science, philosophy etcetera — so that comprehending it fully requires broad general education combined with specialized study material knowledge too. The question will focus on interpreting these complexities correctly — deciphering meaning behind convoluted structures — rather than simply recalling facts presented straightforwardly. ## Rewritten Excerpt "In considering Hadrian’s Wall — constructed ostensibly during AD122 under Emperor Hadrian — one might ponder whether its primary purpose was indeed defensive against northern tribes such as Picts purportedly threatening Roman Britain borders at that time; however speculative historians posit alternatively that this architectural marvel served equally importantly symbolic roles representing Rome’s power projection westward beyond continental Europe shores whilst demarcating cultural boundaries distinctly separating Roman civilization influences southwards versus indigenous northern societies untouched largely yet." ## Suggested Exercise Given Hadrian’s Wall was completed during AD122 under Emperor Hadrian’s rule primarily purportedly defending against northern tribes like Picts threatening Roman Britain borders at that period, Which interpretation aligns most accurately according historians who propose alternate views? A) The wall was purely symbolic representing Rome’s power projection westward beyond continental Europe shores whilst demarcating cultural boundaries distinctly separating Roman civilization influences southwards versus indigenous northern societies untouched largely yet; B) The wall was built mainly out economic reasons aiming at controlling trade routes between north Britain tribes; C) Construction was driven solely by military necessity dictated exclusively by immediate threats posed by nearby hostile forces; D) Its construction was incidental resulting unintentionally during expansionist campaigns unrelated specifically targeting northerly regions; E) None – historians unanimously agree about wall serving primarily defensive purposes without symbolic intentions whatsoever. *** Revision suggestion *** check requirements: - req_no: Advanced knowledge required? revision suggestion: To satisfy this requirement more fully, incorporate explicit references