Lao League stats & predictions

Fuera de Campo: Influencias Culturales y Comunitarias

Más allá del campo, el fútbol en Laos no solo es un deporte; es una parte integral de la cultura local. Los partidos son eventos comunitarios donde amigos y familias se reúnen para compartir momentos inolvidables. La pasión por el fútbol se extiende más allá de las gradas, influenciando la vida cotidiana de los aficionados locales.

Influencia Cultural del Fútbol

• Festividades Locales: Muchos partidos coinciden con festividades locales donde las celebraciones se mezclan con el fervor futbolístico. Estos eventos son una oportunidad perfecta para que las comunidades locales muestren su apoyo incondicional a sus equipos favoritos.
• Educación Juvenil: El fútbol también juega un papel crucial en la educación juvenil. Muchos clubes locales organizan programas destinados a enseñar valores como el trabajo en equipo, la disciplina y el respeto a través del deporte.
• Iniciativas Comunitarias: Los clubes no solo se enfocan en ganar partidos; también están involucrados activamente en iniciativas comunitarias destinadas a mejorar la calidad de vida local mediante proyectos sociales.
• Involucramiento Social:
  
  Cada partido no solo es una exhibición deportiva sino también un evento social donde se fortalecen los lazos comunitarios. Los aficionados comparten historias personales relacionadas con sus equipos mientras disfrutan del espectáculo deportivo.
• Celebraciones Posteriores al Partido:
  
  Cuando termina el partido, las celebraciones continúan fuera del estadio con música tradicional local, comidas típicas compartidas entre amigos y familiares celebrando juntos independientemente del resultado.

  Momentos Históricos Recientes: Clásicos Memorables

  A lo largo de los años han surgido varios clásicos memorables dentro de la Liga Lao que han dejado huella tanto dentro como fuera del campo.

  Cronología de Partidos Históricos

  • Rivalidad entre Lao Toyota y Viséngkham Vientiane:
    
    Sus enfrentamientos siempre han estado cargados de tensión competitiva; cada partido parece tener giros dramáticos e inesperados.
  • Goleada histórica de Bangkok contra Champasak United:
    
    Ese memorable encuentro dejó impresionados a todos debido al número récord [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]: """Define the evaluation metrics.""" [5]: import math [6]: import torch [7]: from fairseq.criterions import register_criterion [8]: from fairseq.criterions.mle import ( [9]: label_smoothed_nll_loss as mle_label_smoothed_nll_loss, [10]: ) [11]: from fairseq.criterions.nat import NTCriterion [12]: from fairseq.dataclass.utils import convert_namespace_to_omegaconf [13]: from omegaconf import II [14]: @register_criterion("stochastic_nats") [15]: class StochasticNATCriterion(NTCriterion): [16]: """ [17]: Stochastic NAT criterion that optimizes for the expected negative log likelihood. [18]: """ [19]: @staticmethod [20]: def add_args(parser): [21]: """Add criterion-specific arguments to the parser.""" [22]: # fmt: off [23]: NTCriterion.add_args(parser) [24]: parser.add_argument('--stochastic-nat-predictions', action='store_true', [25]: help='use stochastic nat predictions') [26]: parser.add_argument('--stochastic-nat-dropout', type=float, [27]: help='dropout probability for stochastic nat predictions') [28]: parser.add_argument('--stochastic-nat-entropy-weight', type=float, [29]: default=0., [30]: help='weight for entropy loss') [31]: # fmt: on [32]: @classmethod [33]: def build_criterion(cls, args): [34]: return cls(args) [35]: def __init__(self, args): [36]: super().__init__(args) [37]: self.args = args [38]: self.stochastic_nat_predictions = getattr(args, "stochastic_nat_predictions", False) [39]: self.stochastic_nat_dropout = getattr(args, "stochastic_nat_dropout", None) ***** Tag Data ***** ID: 1 description: Class definition of StochasticNATCriterion which inherits from NTCriterion. start line: 15 end line: 18 dependencies: - type: Class name: NTCriterion start line: 11 end line: 11 context description: This class extends NTCriterion and defines an advanced stochastic NAT criterion for optimizing expected negative log likelihood. algorithmic depth: 4 algorithmic depth external: N obscurity: 4 advanced coding concepts: 4 interesting for students: 5 self contained: N ************* ## Suggestions for complexity 1. **Dynamic Loss Adjustment**: Implement dynamic adjustment of the negative log likelihood based on real-time feedback or external metrics. 2. **Custom Regularization Term**: Add a custom regularization term that penalizes certain types of errors more heavily than others based on specific criteria. 3. **Multi-task Learning**: Extend the criterion to support multi-task learning where different tasks have different loss functions and weights. 4. **Hierarchical Bayesian Inference**: Incorporate hierarchical Bayesian inference to model uncertainty in the parameters and adjust the loss accordingly. 5. **Adaptive Sampling Strategy**: Develop an adaptive sampling strategy that changes based on the distribution of input data and intermediate results during training. ## Conversation <|user|>I'm working with this piece of code and I want to integrate a custom regularization term that penalizes high variance in predictions more heavily than low variance ones how should I go about it<|end|> <|assistant|>Sure! You can add a custom regularization term within your `StochasticNATCriterion` class to penalize high variance predictions more heavily. Here's how you can modify your existing code: python class StochasticNATCriterion(NTCriterion): """ Stochastic NAT criterion that optimizes for the expected negative log likelihood. """ def __init__(self): super().__init__() self.variance_penalty_weight = nn.Parameter(torch.tensor(1.0)) # Initialize weight for variance penalty def forward(self, model_output): nll_loss = self.compute_nll_loss(model_output) variance_penalty = self.compute_variance_penalty(model_output) total_loss = nll_loss + self.variance_penalty_weight * variance_penalty return total_loss def compute_variance_penalty(self, model_output): # Assuming model_output is logits or similar tensor representing predictions mean_pred = torch.mean(model_output) variance = torch.mean((model_output - mean_pred) ** 2) return variance # Add this method to your existing class where necessary. This code introduces `variance_penalty_weight` as a learnable parameter and computes the variance penalty within the `compute_variance_penalty` method.<|end|> <|user|>great but i want the penalty to be non-linear like using an exponential function to penalize high variance more harshly<|end|> <|assistant|>Understood! You can use an exponential function to penalize high variance more harshly by modifying the `compute_variance_penalty` method: python def compute_variance_penalty(self, model_output): mean_pred = torch.mean(model_output) variance = torch.mean((model_output - mean_pred) ** 2) # Applying exponential function to penalize higher variances more harshly exponential_penalty = torch.exp(variance) - torch.exp(torch.tensor(1e-6)) # Subtracting small value to avoid zero penalty for zero variance return exponential_penalty This modification ensures that higher variances are penalized exponentially more than lower variances.<|end|> <|user|>can we also make this penalty adapt over time like decrease its weight as training progresses<|end|> <|assistant|>Certainly! One way to achieve this is by introducing an adaptive schedule for `variance_penalty_weight`. You could decrease its weight over time using some decay function based on the number of epochs or iterations: python class StochasticNATCriterion(NTCriterion): """ Stochastic NAT criterion that optimizes for the expected negative log likelihood. """ def __init__(self): super().__init__() self.initial_variance_penalty_weight = nn.Parameter(torch.tensor(1.0)) # Initial weight for variance penalty def forward(self, model_output, epoch): nll_loss = self.compute