Liga Prom - Clausura Western Conference North/South stats & predictions

Understanding the Structure

The Liga Prom - Clausura is divided into two main conferences: North and South. Each conference hosts a series of matches that determine the top teams advancing to the playoff stages. The structure is designed to ensure a fair and competitive environment, allowing teams from different regions to showcase their skills on an equal footing.

• North Conference: Known for its aggressive playing style and tactical prowess.
• South Conference: Renowned for its strategic depth and technical skill.

Daily Match Updates

One of the most exciting aspects of following Liga Prom - Clausura is the daily updates on matches. Fans can stay informed about the latest scores, player performances, and match highlights. This constant flow of information keeps the excitement alive and allows supporters to engage with their favorite teams in real-time.

With live updates, fans can track how their teams are performing, analyze key moments from each game, and stay ahead of the competition in terms of knowledge and engagement.

Betting Predictions by Experts

Betting on Liga Prom - Clausura matches is not just about luck; it’s about understanding the game. Expert predictions provide insights based on team form, player statistics, and historical performance. These predictions are invaluable for bettors looking to make informed decisions and increase their chances of winning.

• Team Form Analysis: Understanding recent performances and trends.
• Player Impact: Evaluating key players who can turn the tide of a match.
• Historical Data: Learning from past encounters between teams.

Key Teams to Watch

In both the North and South conferences, several teams stand out as potential champions. Keeping an eye on these teams can provide insights into possible outcomes and exciting matchups.

• North Conference:
  • Tauro FC: Known for their robust defense and strategic gameplay.
  • Chorrillo FC: Renowned for their dynamic offense and quick counter-attacks.
• South Conference:
  • Sporting San Miguelito: Famous for their disciplined approach and tactical flexibility.
  • Alianza FC: Noted for their technical skills and cohesive team play.

The Role of Emerging Talents

Liga Prom - Clausura is not just about established teams; it’s a platform for emerging talents to shine. Young players get the opportunity to prove themselves against seasoned professionals, making it a breeding ground for future stars in Panamanian football.

• Rising Stars: Keep an eye on young players who are making waves with their performances.
• Talent Development: The league plays a crucial role in nurturing new talent and providing them with exposure.

Strategic Insights for Bettors

Betting on Liga Prom - Clausura requires more than just picking winners; it involves understanding strategies and making calculated decisions. Here are some tips for bettors looking to enhance their betting experience:

• Analyze Match Conditions: Consider factors like weather, home advantage, and recent injuries.
• Diversify Bets: Spread your bets across different matches to manage risk.
• Follow Expert Analysis: Stay updated with expert predictions and insights to guide your betting choices.

The Excitement of Playoff Matches

The playoff stage is where the intensity reaches its peak. Teams that have shown resilience throughout the season face off in high-stakes matches that determine who advances to the final rounds. The playoffs are a true test of skill, strategy, and endurance.

• Tension and Drama: Every match is filled with suspense as teams vie for victory.
• Showcase of Talent: Players have the chance to make history with standout performances.

Fan Engagement and Community Spirit

Fans play a crucial role in the success of Liga Prom - Clausura. Their passion fuels the teams and creates an electrifying atmosphere at matches. Engaging with fellow fans through social media, forums, and live events enhances the overall experience of following the league.

• Social Media Interaction: Follow official league accounts for real-time updates and fan interactions.
• Fan Forums: Join discussions with other supporters to share insights and predictions.
• Venue Experience: Attend live matches to experience the thrill firsthand.

Tips for Following Liga Prom - Clausura Matches Online

In today's digital age, following Liga Prom - Clausura online has never been easier. Here are some tips to enhance your online viewing experience:

• Schedule Management: Use online tools to keep track of match schedules across both conferences.
• Livestreams: Access official streams or trusted platforms for high-quality broadcasts.
• Social Media Highlights: Follow hashtags related to specific matches or teams for instant updates.
• Predictive Analytics Tools: Utilize apps that offer advanced analytics for deeper insights into match dynamics.

The Future of Liga Prom - Clausura

The future looks bright for Liga Prom - Clausura as it continues to grow in popularity and attract talent from across Panama. With ongoing investments in infrastructure and youth development programs, the league is poised for further success.

• Growth Initiatives: Efforts are being made to expand viewership both locally and internationally.
• Youth Programs: Initiatives aimed at discovering new talent ensure a steady influx of skilled players.
• # -*- coding: utf-8 -*- import sys import os import json import time import random import numpy as np import tensorflow as tf from tensorflow.contrib import rnn from sklearn import preprocessing # constants CHARS = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-,;.!?:'"/\|_@#$%^&*~`+-=<>()[]{}" PAD_CHAR = " " UNK_CHAR = "~" START_CHAR = "^" END_CHAR = "$" # this one should be equal len(CHARS) + len({PAD_CHAR}) MAX_CHARS = len(CHARS) + len({PAD_CHAR}) + len({UNK_CHAR}) + len({START_CHAR}) + len({END_CHAR}) # Model Hyperparameters HIDDEN_UNITS = 128 BATCH_SIZE = 64 EPOCHS = int(sys.argv[1]) LEARNING_RATE = float(sys.argv[2]) # load data with open('data.json', 'r') as data_file: data = json.load(data_file) # normalize data all_chars = [] for d in data: all_chars += list(d['question']) all_chars += list(d['answer']) le = preprocessing.LabelEncoder() le.fit(all_chars) def get_word_char_list(word): word_char_list = [] for c in word: word_char_list.append(le.transform([c])[0]) return word_char_list def get_word_char_id(char): char_id = le.transform([char])[0] if char_id >= MAX_CHARS: print("Character '%s' has id %d (max allowed id is %d)" % (char, char_id, MAX_CHARS-1)) char_id = le.transform([UNK_CHAR])[0] return char_id def get_random_batch(data): q_batch = [] a_batch = [] for i in range(BATCH_SIZE): rand_idx = random.randint(0, len(data) - BATCH_SIZE) q_batch.append(data[rand_idx]['question']) a_batch.append(data[rand_idx]['answer']) q_char_batch = [] a_char_batch = [] for q,a in zip(q_batch,a_batch): q_char_batch.append(get_word_char_list(q)) a_char_batch.append(get_word_char_list(a)) max_q_len = max([len(q) for q in q_char_batch]) max_a_len = max([len(a) for a in a_char_batch]) q_seq_lens = [len(q) for q in q_char_batch] a_seq_lens = [len(a) + 2 for a in a_char_batch] q_char_ids_padded_batch = np.zeros((BATCH_SIZE,max_q_len), dtype=np.int32) a_char_ids_padded_batch = np.zeros((BATCH_SIZE,max_a_len), dtype=np.int32) for i,(q,a) in enumerate(zip(q_char_batch,a_char_batch)): for j,c in enumerate(q): q_char_ids_padded_batch[i][j] = c for j,c in enumerate(a): a_char_ids_padded_batch[i][j+1] = c return q_seq_lens,a_seq_lens,q_char_ids_padded_batch,a_char_ids_padded_batch # model definition def model(X_question,X_answer,X_question_len,X_answer_len,Y_answer): with tf.variable_scope("model"): with tf.variable_scope("question"): lstm_fw_cell_q=tf.nn.rnn_cell.BasicLSTMCell(HIDDEN_UNITS) lstm_bw_cell_q=tf.nn.rnn_cell.BasicLSTMCell(HIDDEN_UNITS) outputs_q,_=tf.nn.bidirectional_dynamic_rnn(lstm_fw_cell_q,lstm_bw_cell_q,X_question,dtype=tf.float32, sequence_length=X_question_len) X_question_rep=tf.concat(outputs_q,axis=2) X_question_rep=tf.reduce_mean(X_question_rep,axis=1) with tf.variable_scope("answer"): lstm_fw_cell_a=tf.nn.rnn_cell.BasicLSTMCell(HIDDEN_UNITS) lstm_bw_cell_a=tf.nn.rnn_cell.BasicLSTMCell(HIDDEN_UNITS) outputs_a,_=tf.nn.bidirectional_dynamic_rnn(lstm_fw_cell_a,lstm_bw_cell_a,X_answer,dtype=tf.float32, sequence_length=X_answer_len) X_answer_rep=tf.concat(outputs_a,axis=2) X_answer_rep=tf.reduce_mean(X_answer_rep,axis=1) X_input=tf.concat([X_question_rep,X_answer_rep],axis=1) W_h=tf.Variable(tf.truncated_normal([2*HIDDEN_UNITS,HIDDEN_UNITS],stddev=0.1)) b_h=tf.Variable(tf.constant(0.,shape=[HIDDEN_UNITS])) W_z=tf.Variable(tf.truncated_normal([HIDDEN_UNITS,HIDDEN_UNITS],stddev=0.1)) b_z=tf.Variable(tf.constant(0.,shape=[HIDDEN_UNITS])) W_r=tf.Variable(tf.truncated_normal([HIDDEN_UNITS,HIDDEN_UNITS],stddev=0.1)) b_r=tf.Variable(tf.constant(0.,shape=[HIDDEN_UNITS])) W_o=tf.Variable(tf.truncated_normal([HIDDEN_UNITS,HIDDEN_UNITS],stddev=0.1)) b_o=tf.Variable(tf.constant(0.,shape=[HIDDEN_UNITS])) W_softmax=tf.Variable(tf.truncated_normal([HIDDEN_UNITS,len(le.classes_)],stddev=0.1)) b_softmax=tf.Variable(tf.constant(0.,shape=[len(le.classes_)])) def gru(inputs,h_prev): z=rnn.sigmoid(tf.matmul(inputs,W_z)+tf.matmul(h_prev,W_r)+b_z+b_r) r=rnn.sigmoid(tf.matmul(inputs,W_r)+tf.matmul(h_prev,W_r)+b_z+b_r) h_hat=rnn.tanh(tf.matmul(inputs,W_h)+tf.matmul(h_prev* r,W_o)+b_h+b_o) h=h_prev*(1-z)+z*h_hat return h h_prev=tf.zeros([BATCH_SIZE,HIDDEN_UNITS]) logits=[] for t in range(max(Y_answer_len)): input_t=Y_answer[:,t,:] h_prev=gru(input_t,h_prev) logits.append(tf.matmul(h_prev,W_softmax)+b_softmax) logits_stacked=tf.stack(logits,axis=1) return logits_stacked # training operations def train_ops(logits_stacked,Y_answer,Y_answer_len): cross_entropy=[] for t in range(max(Y_answer_len)): current_labels=Y_answer[:,t,:] current_logits=logits_stacked[:,t,:] current_loss=rnn.ctc_loss(current_labels,current_logits,[max(Y_answer_len)],time_major=False) cross_entropy.append(current_loss) cross_entropy_stacked=tf.stack(cross_entropy,axis=1) cross_entropy_reduced=tf.reduce_mean(cross_entropy_stacked) global_step_tensor=tf.Variable(0,name='global_step',trainable=False) update_ops=get_collection('update_ops') with tf.control_dependencies(update_ops): optimizer_op=rnn.RMSPropOptimizer(learning_rate=LEARNING_RATE).minimize(cross_entropy_reduced, global_step=global_step_tensor) return cross_entropy_reduced,optimizer_op if __name__ == '__main__': sess_config=tf.ConfigProto() sess_config.gpu_options.allow_growth=True with tf.Session(config=sess_config) as sess: X_question_ph=tf.placeholder(dtype=tf.int32, shape=[None,None], name='X_question_ph') X_question_ph_one_hot_enc=rnn.one_hot(X_question_ph,len(le.classes_)) X_answer_ph=tf.placeholder(dtype=tf.int32, shape=[None,None], name='X_answer_ph') X_answer_ph_one_hot_enc=rnn.one_hot(X_answer_ph,len(le.classes_)) X_question_len_ph=tf.placeholder(dtype=tf.int32, shape=[None], name='X_question_len_ph') X_answer_len_ph=tf.placeholder(dtype=tf.int32, shape=[None], name='X_answer_len_ph') Y_answer_ph_one_hot_enc=rnn.one_hot(Y_answer_ph,len(le.classes_)) logits=model(X_question=X_question_ph_one_hot_enc, X_answer=X_answer_ph_one_hot_enc, X_question_len=X_question_len_ph, X_answer_len=X_answer_len_ph, Y_answer=Y_answer_ph_one_hot_enc) loss_op,opt_op=train_ops(logits_stacked=logits, Y_answer=Y_answer_ph_one_hot_enc, Y_answer_len=X_answer_len_ph) init_op=tf.global_variables_initializer() sess.run(init_op) print("Start training") start_time=time.time() n_batches_per_epoch=int(len(data)/BATCH_SIZE) print("Number of batches per epoch: ",n_batches_per_epoch) n_epochs_completed=0 while n_epochs_completedyanyunlong/DL-for-Text-Generation<|file_sep|>/homework_2/requirements.txt tensorflow==1.13.2 sklearn==0.19.2 numpy==1.16.4<|repo_name|>yanyunlong/DL-for-Text-Generation<|file_sep|>/homework_2/train.py #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Fri Jun 21 @author: yanyunlong """ import sys import os import json import time import random import numpy as np import tensorflow as tf from tensorflow.contrib import rnn from sklearn import preprocessing # constants CHARS="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-,;.!?:'"/\|_@#$%^&*~`+-=<>()[]{}" PAD_CHAR=" " UNK_CHAR="~" START_CHAR="^" END_CHAR="$" # this one should be equal len(CHARS) + len({PAD_CHAR}) MAX_CHARS=len(CHARS)+len({PAD_CHAR})+len({UNK_CHAR})+len({START_CHAR})+len({END_CHAR}) # Model Hyperparameters HIDDEN_UNITS=int(sys.argv[4]) BATCH_SIZE=int(sys.argv[5]) EPOCHS=int(sys.argv[6]) LEARNING_RATE=float(sys.argv[7]) # load data with open('data.json','r') as data_file: data=json.load(data_file) # normalize data all_chars=[] for d in data: all_chars+=list(d['question']) all_chars+=list(d['answer']) le=preprocessing.LabelEncoder() le.fit(all_chars) def get_word_char_list(word): word_char_list=[] for c in word: word_char_list.append(le.transform([c])[0]) return word_char_list def get_word_char_id(char): char_id=le.transform([char])[0] if char_id>=MAX_CHARS: print("Character '%s' has id %d (max allowed id is %d)"%(char,char_id,MAX_CHARS-1)) char_id=le.transform([UNK_CHAR])[0] return char_id def get_random_batch