M15 Luanda stats & predictions

Exploring the Exciting World of Tennis M15 Luanda Angola

Welcome to the thrilling realm of tennis M15 tournaments in Luanda, Angola. This dynamic category features young talents battling it out on the court, showcasing their skills and determination. With matches updated daily, fans and enthusiasts have a constant stream of fresh action to enjoy. This article delves into the intricacies of these matches, offering expert betting predictions and insights to enhance your viewing experience.

Understanding Tennis M15 Luanda Angola

The Tennis M15 tournament in Luanda is part of the ATP Challenger Tour, specifically designed for players ranked between 301 and 700 globally. These tournaments are crucial stepping stones for players aspiring to climb higher in the rankings. The format typically includes singles and doubles competitions, providing a comprehensive platform for emerging talents.

Daily Match Updates: Stay Informed

One of the most exciting aspects of following Tennis M15 Luanda is the daily updates on matches. This ensures that fans never miss out on any action. Whether you're catching up on results or following live scores, staying informed is easier than ever. Regular updates keep you engaged with the latest developments and surprises from each match.

Expert Betting Predictions: Enhance Your Experience

Betting on tennis adds an extra layer of excitement to watching matches. Expert predictions can guide your decisions, offering insights into potential outcomes based on player form, head-to-head records, and other critical factors. Understanding these predictions can significantly enhance your betting strategy and overall enjoyment of the sport.

Factors Influencing Betting Predictions

• Player Form: Current performance levels are crucial indicators of potential success.
• Head-to-Head Records: Historical matchups can provide valuable insights into likely outcomes.
• Surface Preference: Players often perform better on specific surfaces, influencing match results.
• Injury Reports: Any recent injuries can impact a player's performance significantly.

Detailed Match Analysis: A Deep Dive

To truly appreciate the nuances of Tennis M15 Luanda matches, a detailed analysis is essential. This involves examining player statistics, match history, and other relevant data points. By understanding these elements, fans can gain a deeper appreciation for the strategies employed by players and coaches alike.

Analyzing Player Statistics

• Serving Accuracy: A key component of success in tennis, affecting both offense and defense.
• Rally Lengths: Indicates a player's endurance and ability to maintain pressure during long exchanges.
• Aces Served: A measure of offensive strength and ability to finish points quickly.

Evaluating Match History

• Past Performances: Reviewing previous encounters can highlight patterns and tendencies.
• Tournament Progression: How far players have advanced in past tournaments can indicate their current form.

The Role of Coaches: Behind-the-Scenes Insights

In tennis, coaches play a pivotal role in shaping a player's career. Their strategic input during matches can be decisive in turning the tide in favor of their protégés. Understanding coaching strategies provides additional context to match outcomes and player development.

Critical Coaching Strategies

• Tactical Adjustments: Real-time changes based on opponent behavior and match conditions.
• Mental Preparation: Building resilience and focus to handle high-pressure situations effectively.
• Tactical Drills: Pre-match preparation that enhances specific skills tailored to upcoming opponents.

Fan Engagement: Connecting with the Community

Fans play an integral role in creating an electrifying atmosphere around Tennis M15 Luanda events. Engaging with fellow enthusiasts through forums, social media platforms, or live events fosters a sense of community and shared passion for the sport.

Fostering Fan Communities

• Social Media Groups: Platforms where fans discuss matches, share insights, and connect with others globally.
• Livestream Interactions: Real-time engagement during broadcasts enhances communal viewing experiences.
• Fan Events: Organized gatherings or meetups that bring together local supporters for live matches or discussions.

The Future of Tennis M15 Luanda Angola: Trends to Watch

The landscape of Tennis M15 Luanda is constantly evolving. Emerging trends such as technological advancements in training equipment or analytics-driven strategies are shaping how players prepare for competition. Keeping an eye on these developments ensures that fans stay ahead in understanding what drives success in this competitive arena.

Emerging Trends Impacting Player Performance

• Data Analytics: Utilizing advanced metrics to refine training regimens and match strategies.
• Innovative Training Tools: Adoption of cutting-edge technology for enhanced skill development and injury prevention.
• Nutritional Science Advances: Tailored dietary plans boosting player stamina and recovery rates efficiently.[SNIPPET]<|end|> <|assistant|>`I see you've defined an LSTM-based neural network architecture followed by several Dense layers.` How may I assist you further? Do you need help modifying this architecture or integrating additional functionality?<|end|> <|user|>`I want add attention mechanism after first lstm layer`<|end|> <|assistant|>`Adding an attention mechanism requires defining custom layers since Keras does not have built-in support for attention mechanisms directly compatible with LSTMs.` Here’s how you could add an attention mechanism after your first LSTM layer: Firstly, let's define a custom Attention Layer: python from tensorflow.keras.layers import Layer class Attention(Layer): def __init__(self): super(Attention,self).__init__() def build(self,input_shape): self.W=self.add_weight(name="att_weight",shape=(input_shape[-1],input_shape[-1]),initializer="random_normal") self.b=self.add_weight(name="att_bias",shape=(input_shape[-1],),initializer="zeros") super(Attention,self).build(input_shape) def call(self,x): e=tf.nn.tanh(tf.tensordot(x,self.W,axis=[2],[2])) e=tf.reduce_sum(self.b*e,axis=-1) alpha=tf.nn.softmax(e) context_vector=tf.reduce_sum(alpha[...,tf.newaxis]*x,axis=1) return context_vector Then modify your model definition as follows: python from tensorflow.keras.models import Model inputs = Input(shape=(X_train.shape[1], X_train.shape[2])) lstm_out = LSTM(50)(inputs) attention_out = Attention()(lstm_out) x=Dense(25)(attention_out) x=Dense(10)(x) x=Dense(5)(x) outputs=Dense(1)(x) model=Model(inputs=[inputs], outputs=[outputs]) This will integrate an attention mechanism right after your first LSTM layer.<|end|> <|user|>`Can we use bidirectional lstm instead?`<|end|> <|assistant|>`Certainly! Using Bidirectional LSTMs will allow your model to learn dependencies both forward and backward through time.` Here’s how you can replace your existing LSTM layer with a Bidirectional one: Firstly ensure you have imported `Bidirectional` from `tensorflow.keras.layers`: python from tensorflow.keras.layers import Bidirectional inputs = Input(shape=(X_train.shape[1], X_train.shape[2])) bidir_lstm_out = Bidirectional(LSTM(50))(inputs) x=Dense(25)(bidir_lstm_out) x=Dense(10)(x) x=Dense(5)(x) outputs=Dense(1)(x) model=Model(inputs=[inputs], outputs=[outputs]) This modification wraps your existing `LSTM` layer inside `Bidirectional`, allowing it to process sequences forwards as well as backwards.<|end|> *** Excerpt *** The primary finding was that there were significant differences between genders across all measures taken at baseline; however no significant gender differences were found at post-test when controlling statistically for baseline differences. As seen above gender was found not be significantly related either directly or indirectly through self-efficacy beliefs (Figures ). However given that there were significant baseline gender differences across all measures taken it was decided that further analyses would be undertaken using ANCOVA techniques which control statistically for baseline differences between genders. *** Revision *** ## Plan To create an exercise that is as advanced as possible: - Include technical terms related specifically to statistical methods used (e.g., ANCOVA). - Reference related statistical concepts such as effect size or power analysis without explaining them. - Introduce nested counterfactuals by discussing hypothetical scenarios had different statistical methods been applied. - Incorporate conditionals that require understanding how controlling variables affects outcome interpretation. We could modify the excerpt by embedding more complex language structures such as subjunctive moods or passive constructions where appropriate; introduce more sophisticated vocabulary; include references to specific statistical theories or principles without elaboration; imply rather than state certain relationships; use complex sentence structures requiring careful parsing; reference less common statistical procedures. ## Rewritten Excerpt {"MAIN_EXCERPT": "An initial examination revealed pronounced disparities along gender lines concerning all baseline metrics; notwithstanding this observation, subsequent evaluations indicated no persisting gender-based discrepancies upon employing covariate adjustment methodologies—specifically ANCOVA—to rectify initial variances.nThe aforementioned assessment concluded gender's non-significance regarding direct associations—or those mediated via constructs such as self-efficacy beliefs—as depicted within Figures . Notwithstanding substantial divergences observed at inception across all evaluative criteria predicated upon gender distinctions—thus necessitating further scrutiny—it was adjudged prudent to invoke ancillary analytical paradigms employing ANCOVA techniques aimed at mitigating confounding effects attributable solely to baseline disparities."} ## Suggested Exercise Consider the revised excerpt regarding statistical analysis outcomes from research data involving gender comparisons: "An initial examination revealed pronounced disparities along gender lines concerning all baseline metrics; notwithstanding this observation, subsequent evaluations indicated no persisting gender-based discrepancies upon employing covariate adjustment methodologies—specifically ANCOVA—to rectify initial variances. The aforementioned assessment concluded gender's non-significance regarding direct associations—or those mediated via constructs such as self-efficacy beliefs—as depicted within Figures . Notwithstanding substantial divergences observed at inception across all evaluative criteria predicated upon gender distinctions—thus necessitating further scrutiny—it was adjudged prudent to invoke ancillary analytical paradigms employing ANCOVA techniques aimed at mitigating confounding effects attributable solely to baseline disparities." Given this information: What might be inferred about the research design if it were disclosed that prior studies failed due largely because they did not account adequately for pre-existing differences among participants? A) The researchers might have opted against using ANCOVA if they believed pre-existing differences were negligible. B) The researchers' decision suggests they anticipated potential criticism regarding uncontrolled pre-existing participant differences influencing their results. C) It indicates that ANCOVA was chosen arbitrarily without consideration given its appropriateness compared with other statistical methods. D) It implies that figures illustrating direct associations would inherently lack validity without ANCOVA adjustments due solely to methodological deficiencies. *** Revision *** check requirements: - req_no: 1 discussion: The draft does not require external knowledge beyond understanding ANCOVA, which is mentioned directly within. score: 0 - req_no: 2 discussion: Understanding subtleties about why ANCOVA was chosen requires comprehension, but doesn't extend beyond basic knowledge presented within. score: 2 - req_no: '3' discussion': The excerpt is sufficiently lengthy but could incorporate more complex, embedded ideas requiring deeper analysis.' score': '2' - req_no': '4' discussion': Choices are misleading but could better reflect nuanced misunderstandings.' score': '2' - req_no': '5' discussion': Difficulty level suits advanced undergraduates but doesn't challenge deeply held misconceptions about statistics application.' score': '2' - req_no': '6' discussion': All choices appear plausible without reading question specifics but could benefit from closer alignment with subtle content cues.' score': '2' external fact": Statistical theory relating specifically how different types errors (Type I/II) influence choice between different statistical models under various experimental designs could enrich requirement fulfillment." revision suggestion": To meet requirement one better while enhancing others simultaneously, consider incorporating comparisons between different statistical models like linear regression, logistic regression alongside ANCOVA depending on outcome variable types (continuous, categorical). Introduce concepts like Type I/II errors which affect model selection. This would necessitate external knowledge about error types' impacts on study conclusions, thus deepening comprehension needed beyond excerpt content itself." revised excerpt": ""An initial examination revealed pronounced disparities along gender lines concerning all baseline metrics; notwithstanding this observation, subsequent evaluations indicated no persisting gender-based discrepancies upon employing covariate adjustment methodologiesu2014specifically ANCOVAu2014to rectify initial variances.nThe aforementioned assessment concluded gender's non-significance regarding direct associationsu2014or those mediated via constructs such as self-efficacy beliefsu2014 as depicted within Figures . Notwithstanding substantial divergences observed at inception across all evaluative criteria predicated upon gender distinctionsu2014 thus necessitating further scrutinyu2014it was adjudged prudent to invoke ancillary analytical paradigms employing ANCOVA techniques aimed at mitigating confounding effects attributable solely to baseline disparities."" correct choice": "The researchers' decision suggests they anticipated potential criticism regarding uncontrolled pre-existing participant differences influencing their results." revised exercise": "Considering statistical methodologies used in analyzing research expression outcomes involving gender comparisons detailed above:nnWhat might be inferred expression about researchers' considerations if it were known previous studies failed, expression largely because they did not account adequately for pre-existing differences expression among participants?nnChoices should reflect considerations about Type expression I/II errors potentially impacting study conclusions when choosing among expression different models like linear regression versus logistic regression alongside" incorrect choices: - Researchers might have avoided using ANCOVA if they assumed pre-existing differences insignificant. - Choosing ANCOVA suggests arbitrary preference over other models without considering outcome variable nature. - Figures showing direct associations would lack validity without proper adjustments due only methodological shortcomings. *** Revision_2 *** check requirements: - req_no: 1 discussion: The draft relies primarily on internal content rather than requiring external academic knowledge. -revision suggestion": To enhance requirement fulfillment particularly focusing on requirement one, integrate concepts comparing biases inherent in different types of experimental designs, such as randomized controlled trials versus observational studies which often leadstoerrorsininterpretationifnotproperlyaccountedfor.TheexercisecouldaskhowthechoiceofANCOVAmightbeinfluencedbytheunderlyingstudydesign,andwhatexternalknowledgeaboutstudy designsandtheircommon pitfallswould suggestaboutthis choice." revised exercise": "Considering statistical methodologies used in analyzing research outcomesinvolvinggendercomparisonsdetailedabove:nnHow might awarenessofpotentialbiasesassociatedwithdifferenttypesofstudydesigns(includingrandomizedcontrolledtrialsversusobservationalstudies)affectthechoiceofANCOVAmethodologyforadjustingcovariates,andwhatexternalknowledgeisnecessarytoevaluatethisdecision?" correct choice": Awarenessofpotential biasesinstudydesignssuggeststheuseofANCOVAmightbeaimedatmitigatingconfounderscommoninobservationalstudiesratherthanrandomizedcontrolledtrials,toenhancethevalidityoffindingsregardinggenderdisparities." incorrect choices: - Iftheunderlyingstudywasarandomizedcontrolledtrial,itmightindicatealessneedforANCOVAbecauserandomizationminimizescovariatesbiasbydefault,suggestingothermethodscouldhavebeenequallyeffectiveifnotmoresodependingonthespecificoutcomevariabletypeconsidered.(logisticvs.linearregression). - ChoosingANCOVAdemonstratesanimplicitassumptionthatallrelevantconfounderswereknownandmeasuredatbaseline,suggestingadeepunderstandingofthevariablesatplaybutpotentiallyoverlookinglatentvariablesnotaccountedforwhichcouldbiasresultsevenafteradjustment.(conceptualerror). <>Hey AI assistant i am trying wrap my head around some python code here im working wth something called `get_method_body`. It uses recursion memoization n stuff idk fully yet... Can u explain how exactly does memoization work here especially since its dealing w recursive calls? heres what i got so far: python def get_method_body(method_name): if method_name == None: return None if method_name == "" or method_name == "" : return None if method_name.startswith("lambda$"): return None if len(method_cache.get(method_name)) >0: return method_cache.get(method_name) for c_methd_body_node_list_node_element_node_list_element_node_child_node_child_node_list_element_node_body_node_method_or_field_decl__node_element_ref__node_element_ref__node_element_ref__node_element_ref__node_element_ref__node_element_ref__name_expr_simple() in methd_body_node_list: if c_methd_body_node_list_node_element_node_list_element_node_child_node_child_node_list_element_node_body_node_method_or_field_decl__node_element_ref__node_element_ref__node_element_ref__node_element_ref__node_element_ref__node_element_ref__name_expr_simple().get_method_name() ==method_name: method_cache.put(method_name,c_methd_body_node_list_node_element_node_list_element_node_child_node_child_node_list_element_node_body_nodemethod_or_field_decl__node_elemenntref___nodereference___nodereference___nodereference___nodereference___nodereference___nameexprsimple()) return c_methd_body_nodelist_nodeselement_nodelistelement_nodetchild_nodchild_nodelistelement_nobody_nodemethodorfielddcl_nodereferencerefrefrefrefrefnameexprsimple() break for c_classbodydecl_parent____methodelemnt_parent____methodelemnt_parent____methodelemnt_parent____methodelemnt_parent____methodelemnt_parent____methodelemnt_parent_____methoddecl() in classbodydecl_parent_methodelement_parent_methodelement_parent_methodelement_parent_methodelement_parent_methodelement_parent_methodelement_methoddecl(): if c_classbodydecl_parenparentparentparentparentparentparentmethoddecl().get_method_name() ==method_name: method_cache.put(method_name,c_classbodydecl_parenparentparentparentparentparentparenmethoddecl()) return c_classbodydecl_parenparentparenparenparenparenparenparenmethoddecl() break raise Exception("Method "+method_name+" not found") <>Sure! Let's break down how memoization works in your `get_method_body` function. ### Memoization Overview: Memoization is an optimization technique used primarily to speed up computer programs by storing the results of expensive function calls and returning the cached result when the same inputs occur again. ### How It Works Here: In your code snippet: python if len(method_cache.get(method_name)) >0: return method_cache.get(method_name) This part checks if there's already a cached result for `method_name`. If so, it returns immediately without recalculating. ### Recursive Calls: The function seems designed recursively through its structure but doesn't explicitly call itself within this snippet. Instead, recursion might occur elsewhere if `get_method_body` is called within loops or other functions handling similar tasks. ### Caching Results: When a result is computed: python method_cache.put(method_name,c_some_complex_expression()) return c_some_complex_expression() It stores (`put`) this result into `method_cache`, associating it with `method_name`. Next time you call `get_method_body` with the same `method_name`, it retrieves (`get`) this cached result instead of recomputing everything. ### Key Points: - **Avoid Redundancy:** By caching results, redundant calculations are avoided when processing repeated requests. - **Efficiency:** Especially beneficial when dealing with recursive functions where calculations overlap frequently. If you need more details about specific parts or further clarification on recursion here, feel free to ask! 😊<>: Hi there! I'm trying out some TensorFlow operations related matrix multiplications using Kronecker products between two matrices [email protected] @ [email protected] @ [email protected] + [email protected] @ [email protected] @ [email protected] + [email protected] @ [email protected] @ [email protected] + [email protected] @ [email protected] @ [email protected] , which has been expanded manually using distributive properties before converting them back into tf.matmul calls again later below... But now my head hurts trying figure out what went wrong... Can someone help me debug why my output isn't matching expected results? Here's my buggy code snippet: python def kronecker_product_expanded(matrices): """Compute Kronecker product expansion manually.""" A_B_T_E_F_T_G_H_T_C_D_T_E_F_T_G_H_T_A_B_T_C_D_T_G_H_T_A_B_T_E_F_T_H_I_T = tf.matmul(tf.matmul(A_B_T_E_F_T_G_H_T(), C_D_T_E_F_T_G_H_T()), tf.matmul(A_B_T_C_D_T_G_H_T(), A_B_T_E_F_T_H_I_T())) return kronecker_product_expanded([A,B,C,D,E,F,G,H,I]) And here's what I get when I run it: Traceback (most recent call last): File "kronecker_debug.py", line xxx xx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxx xxxxxxxxxx xxxx xxxx xxxxx xxxxx xxxxxxxxxx xx xxxxxx x xxxxx x x xx x xx x x xx x x x x xxxx x xxxx xxxxxxxxx xx xxxx xxxx xxxxxxxxx xxxx xx xxxx XXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXX XXXXXXXXX XXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXX XX XX XX XX XX XX XX XXXXXXXXX XXXXXXXXX XXXXXXXXXX ERROR TypeError Traceback (most recent call last) File "kronecker_debug.py", line xx ,in kronecker_product_expanded return tf.matmul(tf.matmul(A_B_T_E_F_T_G_H_T(), C_D_T_E_F_T_G_H_T()), tf.matmul(A_B_T_C_D_t_G_H_t(), A_B_t_E_f_t_h_i_t())) TypeError:'NoneType' object is not callable Could someone point out what I'm missing here? Thanks! <>: python It looks like there are several issues causing errors in your code snippet: 1. **Function Definitions Missing**: You're calling functions like `A_B_T_E_F_T_G_H_t()` without defining them first. Ensure each operation like `[email protected]` is defined separately before calling them. 2. **Variable Naming Confusion**: Your variable names are overly long and confusing (`A_B_t_E_f_t_h_i_t`). Consider breaking them down into smaller parts or using intermediate variables. 3. **NoneType Error**: The error `'NoneType' object is not callable` suggests one of these operations returns `None`. Double-check each matrix multiplication step returns a valid tensor. Here’s a simplified approach: python import tensorflow as tf def kronecker_product_expanded(A, B, C, D, E, F, G, H, I): ABT = tf.matmul(A, B.transpose()) CDT_ETFTGHT_ABTCDTGHT_ABTETFTHIT = tf.matmul(tf.matmul(ABT @ E.transpose() @ F.transpose() @ G.transpose() @ H.transpose(), C @ D.transpose() @ E.transpose() @ F.transpose() @ G.transpose() @ H.transpose()), tf.matmul(tf.matmul(A @ B.transpose(), C @ D.transpose()) @ G.transpose() @ H.transpose(), ABT @ E.transpose() @ F.transpose() @ H.transpose())) return CDTEFTGHT_ABTCDTGHT_ABTETFTHIT # Example usage with initialized matrices A-I should be provided here... Make sure each matrix operation returns a valid tensor before proceeding with further multiplications. Also ensure TensorFlow operations are correctly used (`tf.linalg.matmul` instead of Python’s native operators). 😊 Let me know if you need further clarification! <>: Got it! I think breaking down those operations into smaller parts will help avoid confusion. Also switching backslashes might make things clearer too since Python doesn't support operator chaining like MATLAB does anyway right? Let me try refactoring it like this: python import tensorflow as tf def kronecker_product_expanded(A,B,C,D,E,F,G,H,I): ABT = tf.linalg.matmul(A,B,T) CDTFETFGHT_ABTCDTGHT_ABTETFTHIT = tf.linalg.matmul( tf.linalg.matmul( ABT, tf.linalg.matmul(E,T,F,T,G,T,H,T), CDTEFTGHT_ABTCDTGHT_ABTETFTHIT = tf.linalg.matmul( ABT, CDTEFTGHT_ABTCDTGHT_ABTETFTHIT = tf.linalg.matmul( ABT, CDTEFTGHT_ABTCDTGHT_ABTETFTHIT = tf.linalg.matmul( ABT, ETFHIT = tf.linalg.matmul( EF, HT))))))) return CDTFETFGHT_ABTCDTGHT_ABTETFTHIT # Example usage with initialized matrices A-I should be provided here... Does this look better? Let me know if there's anything else I should tweak! <> Sure! Your refactored code looks much clearer now by breaking down operations into smaller steps using TensorFlow functions correctly (`tf.linalg.matmul`). Here are some additional tweaks: python import tensorflow as tf def kronecker_product_expanded(A,B,C,D,E,F,G,H,I): ABT = tf.linalg.matmul(A,B,T) EFTHT = tf.linalg.matmul(E,F,T,H,T) GHITTGFHITTCDTFETFGHT_ABTCDTGHT_ABTETFTHIT = tf.linalg.matmul( ABT, EFTHT) CDTEFTFGHITT_CDTEFTFGHITT_CDTEFTFGHITT = tf.linalg.matmul(C,D,T), EFTHT) GHITT_CDTEFTFGHITT_CDTEFTFGHITT_CDTEFTFGHITT = tf.linalg.matmul(G,H,T), CDTEFTFGHITT_CDTEFTFGHITT_CDTEFTFGHITT) CDTFETFGHT_ABTCDTGHT_ABTETFTHIT = tf.linalg.matmul( GHITT_CDTEFTFGHITT_CDTEFTFGHITT_CDTEFTFGHITT, GHITT_CDTEFTFGHITT_CDTEFTFGHITT_CDTEFTFGHITT) GHITHEFHI = tf.linalgebra.matml(E,F,T,I,T) FINAL_RESULT = tf.linalgebra.matml( ABT, GHITHEFHI) return FINAL_RESULT # Example usage with initialized matrices A-I should be provided here... Make sure each intermediate variable represents a valid tensor operation before proceeding further down the chain. Also ensure consistent naming conventions throughout your code. Let me know if there’s anything else you’d like assistance with! 😊<>: Hi there! I'm working on implementing Tarjan's algorithm recursively using continuation-passing style (CPS), but I'm running into some issues that I just can't figure out. Here's what my current implementation looks like: python def visit(u): def set_root(v): roots[v] |= u not in visited_ancestors[v] # False & True -> False | True -> True visited_ancestors[v] |= {u} # {u} | {u,v} -> {u,v} def clear_root(v): roots[v] &= u not in visited_ancestors[v] # True & False -> False | False -> False visited_ancestors[v] -= {u} # {u,v} - {u} -> {v} def visit_(v,w): if v not in visited: visit(v) roots[v] |= roots[w] elif v != w:# TODO optimize case w seen but unexplored Ancestors[u]cap Ancestors[w]==empty ==> Roots[u]&~Roots[w]==Roots[u] clear_root(v) roots[v] |= ~roots[w] & roots[u] mark[u]=len(orderings)+len(ordering)**kwargs.get('multiterm',False)*id(u) orderings.append(u) visited.add(u) visited_ancestors[u].add(u) children=u.expand(arities,{}) if len(children)==0:# leaf ordering.append(u) clear_root(u) return futures=[visit_(child,u) if child!=u else child.visit(set_root,set_clear_root,None,True)# Passing set_clear_root instead set_root here seems wrong? for child,_ignores_inchildren in children.items()] [f.result() for f,_ignores_result_value_basedness awaitables_of_visit_children_generated_by_computation_graph_ordering_choice_in_point_u_children_items_future_in_futures_and_f_result_in_ f.result() ] ordering.append(u) del orderings[-ordering_contains(u)] clear_root(u) visit(start_vertex_id_here) result=u"n".join("{} {}".format(len(o), o )for o,_ignores_rest_inorderings_inorderings_orderings_and_orderings_orderings_orderings_reverse_mapping_and_result_of_join_format_and_len_o_and_o_and_result_of_join_format_and_len_o_and_o_and_result_of_join_format_and_len_o_and_ o _ignores_rest_inorderings_inorderings_orderings_and_orderings_orderings_orderings_reverse_mapping_and_result_of_join_format_and_len_o_and_o_and_result_of_join_format_and_len_o_and_ ) print(result); sys.stdout.flush() assert(len(order)=len(arities)) reverse_mapping=dict(zip(order,o.range)) return reverse_mapping raise AssertionError(warning_message.format(node_type_id=str(id(node_type)),arg_id=str(id(arg)),argument=a)) else:# multiterm raise NotImplementedError(warning_message.format(node_type_id=str(id(node_type)),arg_id=str(id(arg)),argument=a)) else:# single term assert(isinstance(a,(gast.Name,gast.Attribute))) yield g,a.id,None,[a.lineno,a.col_offset,g.scopeName()] raise Exception(warning_message.format(node_type_id=str(id(node_type)),arg_id=str(id(arg)))) else:# single term assert(isinstance(a,(gast.Name,gast.Attribute))) yield g,a.id,None,[a.lineno,a.col_offset,g.scopeName()] raise Exception(warning_message.format(node_type_id=str(id(node_type)),arg_id=str(id(arg)))) else:# single term assert(isinstance(a,(gast.Name,gast.Attribute))) yield g,a.id