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Primera Federacion - Group 1 stats & predictions

The Thrilling Landscape of Primera Federación - Group 1 Spain

The Primera Federación - Group 1 Spain is a vibrant league that captures the essence of Spanish football, featuring passionate teams and thrilling matches. As we look ahead to tomorrow's fixtures, fans and bettors alike are eager to witness the unfolding drama on the pitch. With expert betting predictions in hand, let's dive into the exciting world of tomorrow's matches.

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Upcoming Matches: A Detailed Overview

Tomorrow promises a series of captivating encounters in the Primera Federación - Group 1 Spain. Each match is not just a test of skill but also a strategic battle, where every move can tip the scales in favor of one team or another.

  • Match 1: Team A vs. Team B
  • Match 2: Team C vs. Team D
  • Match 3: Team E vs. Team F
  • Match 4: Team G vs. Team H

Expert Betting Predictions: Analyzing the Odds

In the realm of sports betting, predictions are as much an art as they are a science. Expert analysts have delved deep into statistics, team form, and player performances to provide insights that can guide your betting decisions.

Match 1: Team A vs. Team B

Team A has been in excellent form recently, securing back-to-back victories. Their attacking prowess, led by their star striker, makes them favorites in this clash. However, Team B's solid defense could pose a significant challenge.

  • Prediction: Over 2.5 goals
  • Odds: 1.75

Match 2: Team C vs. Team D

This match is expected to be a tightly contested affair. Both teams have shown resilience throughout the season, but Team C's home advantage might give them the edge.

  • Prediction: Draw
  • Odds: 3.20

Match 3: Team E vs. Team F

Team E has been struggling with injuries, which could impact their performance against an in-form Team F. The latter's recent victories suggest they are well-prepared for this encounter.

  • Prediction: Under 2.5 goals
  • Odds: 1.90

Match 4: Team G vs. Team H

Known for their aggressive playstyle, Team G will look to dominate possession against a defensively robust Team H. This clash could be decided by individual brilliance.

  • Prediction: Both teams to score
  • Odds: 2.10

Tactical Insights: What to Watch For

Each match in the Primera Federación - Group 1 Spain offers unique tactical battles that can influence the outcome significantly. Understanding these nuances is key to making informed predictions.

Tactics of Team A vs. Team B

Team A is expected to leverage their high pressing game to disrupt Team B's rhythm. Their midfield dynamism will be crucial in maintaining control and creating scoring opportunities.

Tactics of Team C vs. Team D

Both teams are likely to adopt a cautious approach, focusing on maintaining a solid defensive structure while looking for counter-attacking opportunities.

Tactics of Team E vs. Team F

With key players missing due to injury, Team E might adopt a more conservative strategy, relying on set-pieces as a potential source of goals.

Tactics of Team G vs. Team H

Expect an intense midfield battle as both teams vie for dominance in this area. Quick transitions and fast-paced attacks will be pivotal in breaking down defenses.

Key Players to Watch: Stars Who Could Make a Difference

Team A's Star Striker

Known for his clinical finishing and agility, this player is always a threat whenever he gets on the ball. His ability to find space in tight defenses could be decisive.

Team B's Defensive Anchor

A stalwart in defense, this player’s leadership and tackling prowess will be vital in containing Team A’s attack.

Team C's Creative Midfielder

With exceptional vision and passing accuracy, he is the creative force behind many of their attacks.

Team D's Goalkeeping Talent

His reflexes and shot-stopping abilities make him one of the league’s top goalkeepers.

<|repo_name|>benjaminbeaujean/EEG-IV<|file_sep|>/README.md # EEG-IV Code for "EEG-Informed Variational Inference" paper. This code is written with Python 3. To install all dependencies run: pip install -r requirements.txt ## Getting Started To train an IV using EEG data run: python train_IV.py To train an IV using fMRI data run: python train_IV_fmri.py To evaluate an IV trained on EEG data run: python eval_IV.py --model_dir PATH_TO_MODEL_DIRECTORY To evaluate an IV trained on fMRI data run: python eval_IV_fmri.py --model_dir PATH_TO_MODEL_DIRECTORY ## Reproducing Results ### EEG To reproduce results reported in Table I for EEG data run: python train_IV.py --dataset eeg --num_epochs 500 --save_every_n_epochs 50 --lr=0.0005 --batch_size=512 --weight_decay=0 --beta=0 --kl_weight=0 python eval_IV.py --model_dir PATH_TO_EEG_MODEL_DIRECTORY To reproduce results reported in Table II for EEG data run: python train_IV.py --dataset eeg --num_epochs 500 --save_every_n_epochs 50 --lr=0.0005 --batch_size=512 --weight_decay=0 --beta=0 --kl_weight=0 python eval_IV.py --model_dir PATH_TO_EEG_MODEL_DIRECTORY --kl_weight=0 python train_IV.py --dataset eeg --num_epochs 500 --save_every_n_epochs 50 --lr=0.0005 --batch_size=512 --weight_decay=0 --beta=1e-4 --kl_weight=0 python eval_IV.py --model_dir PATH_TO_EEG_MODEL_DIRECTORY_1E-4BETA_500EPOCHS_512BATCHSIZE_0005LR_0WEIGHTDECAY_000KLWEIGHT/eval/eval_500.pkl python train_IV.py --dataset eeg --num_epochs 500 --save_every_n_epochs 50 --lr=0.0005 --batch_size=512 --weight_decay=0 --beta=1e-8 --kl_weight=0 python eval_IV.py --model_dir PATH_TO_EEG_MODEL_DIRECTORY_1E-8BETA_500EPOCHS_512BATCHSIZE_0005LR_0WEIGHTDECAY_000KLWEIGHT/eval/eval_500.pkl python train_IV.py --dataset eeg --num_epochs 500 --save_every_n_epochs 50 --lr=0.0005 --batch_size=512 --weight_decay=0 --beta=1e-6 python eval_IV.py --model_dir PATH_TO_EEG_MODEL_DIRECTORY_1E-6BETA_500EPOCHS_512BATCHSIZE_0005LR_0WEIGHTDECAY_000KLWEIGHT/eval/eval_500.pkl python train_IV.py --dataset eeg --num_epochs 10000 --save_every_n_epochs 1000 --lr=0.0005 --batch_size=512 --weight_decay=1e-4 --beta=None --kl_weight=None --final_beta_annealing_start_epoch=9000 --final_beta_annealing_end_epoch=None --final_beta_annealing_final_value=None --final_kl_weight_annealing_start_epoch=None --final_kl_weight_annealing_end_epoch=None --final_kl_weight_annealing_final_value=None python eval_IV.py --model_dir PATH_TO_EEG_MODEL_DIRECTORY_NONEBETA_NONEKLWEIGHT_FINALBETAANNEALINGSTARTAT900ENDAT10000FINALVALUE100KLENDATANNEALINGSTARTENDVALUENONE/eval/eval.pkl --kl_weight=None --beta=None --final_beta_annealing_start_epoch=9000 --final_beta_annealing_end_epoch=None --final_beta_annealing_final_value=None --final_kl_weight_annealing_start_epoch=None --final_kl_weight_annealing_end_epoch=None --final_kl_weight_annealing_final_value=None --return_samples=True --num_samples_to_return_for_test_set_evaluation=-1 ### fMRI To reproduce results reported in Table III for fMRI data run: python train_IV_fmri.py python eval_IV_fmri.py PATH_TO_FMRI_MODEL_DIRECTORY ## Citation If you use this code or data please cite our paper: bibtex @inproceedings{beaujean2021eeg, title={EEG-Informed Variational Inference}, author={Beaujean, Benjamin and Sainath, Thilo}, booktitle={International Conference on Learning Representations}, year={2021} } ## Acknowledgements Thanks to Thilo Sainath for his guidance on this project. <|file_sep|># Copyright (c) Facebook, Inc. and its affiliates. # All rights reserved. # # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. """General-purpose neural network layers.""" import math import torch from torch import nn import torch.nn.functional as F class Flatten(nn.Module): """Flattens inputs.""" def __init__(self): super().__init__() def forward(self, x): return x.view(x.size(0), -1) class Linear(nn.Linear): """Linear layer with weight normalization.""" def __init__(self, *args, init_gain: float = None, **kwargs): super().__init__(*args, **kwargs) if init_gain is not None: nn.init.xavier_uniform_(self.weight.data) self.weight.data *= init_gain if self.bias is not None: self.bias.data.zero_() class LinearNoBias(nn.Linear): """Linear layer with no bias.""" def __init__(self, *args, **kwargs): super().__init__(*args, bias=False, **kwargs) class ConvTranspose2d(nn.ConvTranspose2d): """Convolutional transpose layer with weight normalization.""" def __init__(self, *args, init_gain: float = None, **kwargs): super().__init__(*args, **kwargs) if init_gain is not None: nn.init.xavier_uniform_(self.weight.data) self.weight.data *= init_gain class ResidualBlock(nn.Module): """Residual block.""" def __init__(self, n_in_channels: int, n_out_channels: int, kernel_size: int = None, stride: int = None, padding: int = None): super().__init__() # check if we need to adjust number of channels same_n_channels = n_in_channels == n_out_channels # build convolutional layers self.conv_layers = nn.Sequential( ConvTranspose2d(n_in_channels, n_out_channels, kernel_size, stride=stride or kernel_size // 2, padding=(kernel_size - stride) // 2 if stride else padding or kernel_size // 2), nn.ReLU(True)) # build projection layer self.projection = (n_in_channels != n_out_channels) or (stride and stride > 1) if self.projection: self.projection_layer = ConvTranspose2d(n_in_channels, n_out_channels, kernel_size=1, stride=stride or kernel_size // 2) def forward(self, x): h = self.conv_layers(x) if self.projection: x = self.projection_layer(x) return h + x class UpsampleConvLayer(nn.Sequential): """Upsampling followed by convolutional layer.""" def __init__(self, n_in_channels: int, n_out_channels: int, kernel_size: int = None): super().__init__() # add upsampling self.add_module('upsample', nn.Upsample(scale_factor=2)) # add convolutional layer self.add_module('conv', ConvTranspose2d(n_in_channels, n_out_channels, kernel_size)) class UpsampleConvBlock(nn.Module): """Upsampling followed by convolutional block.""" def __init__(self, n_in_channels: int, n_out_channels: int): super().__init__() # add upsampling followed by convolutional layer self.add_module('upsample_conv', UpsampleConvLayer(n_in_channels // 2 + n_in_channels, n_in_channels // 2)) # add residual block self.add_module('residual_block', ResidualBlock(n_in_channels // 2 + n_in_channels // 2, n_out_channels)) class DiscriminatorConvBlock(nn.Module): """Discriminator convolutional block.""" def __init__(self, n_in_channels: int, n_out_channels: int): super().__init__() # add convolutional layer with strides self.add_module('conv_stride', ConvTranspose2d(n_in_channels, n_out_channels // 2 + n_out_channels, kernel_size=4, stride=2, padding=int(math.floor(4 / 2)), bias=False)) class Encoder(nn.Module): """Encoder network.""" def __init__(self, latent_dims: int = (16,), input_shape=(48,), base_filters: int =16): super(Encoder,self).__init__() assert len(input_shape) >= 1 and len(latent_dims) >= 1 self.latent_dims = latent_dims number_of_layers = len(latent_dims) # build encoder layers modules = [] # input layer, assuming square input current_input_shape = input_shape[1] modules.append(ResidualBlock(input_shape[0], base_filters)) self.input_layer_squeezed_shape = base_filters * current_input_shape * current_input_shape for i in range(number_of_layers - special_first_layer - special_last_layer): # hidden layers modules.append(ResidualBlock(latent_dims[i], latent_dims[i+1])) current_input_shape = current_input_shape // 2 # special layer for mean modules.append(LinearNoBias(self.input_layer_squeezed_shape, latent_dims[-1])) # construct encoder self.encoder_layers = nn.Sequential(*modules) # store latent dimension sizes self.latent_dims_sizes = [self.input_layer_squeezed_shape] + [dims * current_input_shape * current_input_shape for dims in latent_dims[:-1]] + [latent_dims[-1]] def encode(self,x): results = [] for i,layers in enumerate(self.encoder_layers._modules.values()): x = layers(x) if i == len(self.encoder_layers._modules)-1: pass else: results.append(x.view(x.size(0),-1)) return results,zs[-1] class Decoder(nn.Module): def __init__(self, latent_dims=(16,), input_shape=(48,), base_filters:int =16): super(Decoder,self).__init__() assert len(input_shape) >= 1 and len(latent_dims) >= 1 number_of_layers = len(latent_dims) self.latent_dims = latent_dims modules=[] self.input_layer_squeezed_shape = base_filters * (input_shape[1]//4) * (input_shape[1]//4) current_input_dim = base_filters # special last layer modules.append(Linear(latent_dims[-1],self.input_layer_squeezed_shape)) # hidden layers + res blocks for i in range(number_of_layers - special_first_layer - special_last_layer): modules.append(UpsampleConvBlock(current_input_dim,current_input_dim*2)) current_input_dim *=2 modules.append(ResidualBlock(current_input_dim,input_shape[0])) # construct decoder self.decoder_layers = nn.Sequential(*modules) def decode(self,zs): x=self.decoder_layers(zs) return x def reparameterize(mu,sigma): std=sigma.exp() eps=torch.randn_like(std) return eps.mul(std).add_(mu) def log_sum_exp(value,dim=None,dtype=None,out=None):