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2. Lig - Red Group Insights & football Betting Overview
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Upcoming Matches in Turkey's 2. Lig Red Group: Tomorrow's Predictions and Insights
The excitement surrounding the Turkish 2. Lig Red Group is at an all-time high as teams gear up for tomorrow's matches. With each game carrying significant weight in the league standings, fans and bettors alike are keenly analyzing potential outcomes. Here, we delve into expert predictions, betting insights, and key matchups that could define the day's results.
Match Highlights
The 2. Lig Red Group promises thrilling encounters with several clubs vying for crucial points to improve their standings. Let's explore the key matches and what to expect from each.
Bursa Merinosspor vs. Eskişehirspor
This clash between Bursa Merinosspor and Eskişehirspor is one of the most anticipated fixtures of the day. Bursa Merinosspor, known for their solid defensive strategy, will be looking to capitalize on their home advantage against a resilient Eskişehirspor side.
- Bursa Merinosspor: With a strong home record, they are expected to leverage their familiarity with the pitch to control the game.
- Eskişehirspor: Despite being away, their recent form suggests they could pose a significant challenge.
Kayserispor vs. Kırklarelispor
Kayserispor, a team with a rich history, faces Kırklarelispor in what promises to be a tactical battle. Both teams have shown resilience throughout the season, making this match a potential turning point for either side.
- Kayserispor: Their ability to adapt to different playing styles will be crucial against Kırklarelispor.
- Kırklarelispor: Known for their aggressive play, they will look to exploit any weaknesses in Kayserispor's defense.
Betting Predictions and Insights
With each match offering unique betting opportunities, it's essential to consider expert predictions and statistical analyses before placing bets.
Key Betting Tips
- Over/Under Goals: Matches like Bursa Merinosspor vs. Eskişehirspor may see fewer goals due to defensive strategies, making an under bet appealing.
- Both Teams to Score (BTTS): In games with aggressive offenses like Kayserispor vs. Kırklarelispor, betting on BTTS could be lucrative.
- Correct Score: Analyzing recent performances can help predict exact scores, especially in tightly contested matches.
Player Performances to Watch
Individual brilliance often turns the tide in closely fought matches. Here are some players whose performances could be pivotal tomorrow.
Bursa Merinosspor - Key Player: Ali Demir
Ali Demir has been instrumental in Bursa Merinosspor's midfield dominance. His ability to distribute the ball effectively and break opposition lines makes him a player to watch.
Eskişehirspor - Key Player: Emre Özkan
Emre Özkan's pace and dribbling skills make him a constant threat on the wings. His contributions could be crucial in breaking down Bursa's defense.
Tactical Analysis
Understanding the tactical setups of each team can provide deeper insights into how tomorrow's matches might unfold.
Bursa Merinosspor's Defensive Strategy
Bursa Merinosspor is likely to adopt a compact defensive formation, focusing on limiting space for Eskişehirspor's attackers. Their full-backs will play a crucial role in maintaining width while preventing counter-attacks.
Eskişehirspor's Counter-Attacking Play
Eskişehirspor may rely on quick transitions and exploiting spaces left by Bursa's attacking forays. Their forwards will need to be sharp in converting chances created from counters.
Kayserispor's Midfield Battle
The midfield clash between Kayserispor and Kırklarelispor will be central to the game's outcome. Both teams possess technically gifted midfielders who can dictate the tempo of play.
- Kayserispo<|repo_name|>zhaofengwen/PyCascades<|file_sep|>/pycascades/ldamodel.py
# -*- coding: utf-8 -*-
"""
Created on Thu Dec 27 12:57:55 2018
@author: chengy
"""
from __future__ import print_function
import numpy as np
import gensim
from gensim.models import LdaModel
from gensim.models.coherencemodel import CoherenceModel
class LdaModelWrapper():
def __init__(self,
corpus,
num_topics,
alpha='symmetric',
eta=None,
id2word=None,
eval_every=0,
iterations=50,
gamma_threshold=0.001,
minimum_probability=0.01,
passes=1,
random_state=None,
chunksize=10000,
decay=0.5,
offset=1.0,
eval_every_iter=10):
self.corpus = corpus
self.num_topics = num_topics
self.alpha = alpha
self.id2word = id2word
self.eval_every = eval_every
self.iterations = iterations
self.gamma_threshold = gamma_threshold
self.minimum_probability = minimum_probability
self.passes = passes
self.random_state = random_state
self.chunksize = chunksize
self.decay = decay
self.offset = offset
self.eval_every_iter = eval_every_iter
def train(self):
lda_model = LdaModel(corpus=self.corpus,
num_topics=self.num_topics,
id2word=self.id2word,
alpha=self.alpha,
eta=self.eta,
eval_every=self.eval_every,
iterations=self.iterations,
gamma_threshold=self.gamma_threshold,
minimum_probability=self.minimum_probability,
passes=self.passes,
random_state=self.random_state,
chunksize=self.chunksize,
decay=self.decay,
offset=self.offset)
return lda_model
def compute_coherence(self, lda_model):
coherence_model_lda = CoherenceModel(model=lda_model,
texts=self.corpus_texts,
dictionary=self.dictionary,
coherence='c_v')
coherence_lda = coherence_model_lda.get_coherence()
return coherence_lda
if __name__ == '__main__':
documents_raw = ['Human machine interface for lab abc computer applications',
'A survey of user opinion of computer system response time',
'The EPS user interface management system',
'System and human system engineering testing of EPS',
'Relation of user perceived response time to error measurement',
'The generation of random binary unordered trees',
'The intersection graph of paths in trees',
'Graph minors IV Widths of trees and well quasi ordering',
'Graph minors A survey']
# remove common words and tokenize
stoplist = set('for a of the and to in'.split())
texts = [[word for word in document.lower().split() if word not in stoplist]
for document in documents_raw]
# remove words that appear only once
from collections import defaultdict
frequency = defaultdict(int)
for text in texts:
for token in text:
frequency[token] += 1
texts = [[token for token in text if frequency[token] > 1]
for text in texts]
# transform corpus into BoW format (list of (token_id, token_count) tuples)
dictionary = gensim.corpora.Dictionary(texts)
corpus_bow = [dictionary.doc2bow(text) for text in texts]
# fit LDA model using Gensim package
lda_model_gensim_10topics = LdaModel(corpus=corpus_bow,num_topics=10,id2word=dictionary)
# fit LDA model using PyCascades package
lda_model_wrapper_10topics = LdaModelWrapper(corpus=corpus_bow,num_topics=10,id2word=dictionary)
lda_model_py_cascades_10topics = lda_model_wrapper_10topics.train()
# print out topics
print("LDA model trained by Gensim package")
print(lda_model_gensim_10topics.print_topics(num_topics=10,num_words=3))
print("LDA model trained by PyCascades package")
print(lda_model_py_cascades_10topics.print_topics(num_topics=10,num_words=3))
<|file_sep|># -*- coding: utf-8 -*-
"""
Created on Thu Dec 27 13:21:40 2018
@author: chengy
This script provides an implementation of Gibbs sampling algorithm.
"""
from __future__ import print_function
import numpy as np
class GibbsSampler():
def __init__(self,n,m,k,a,b):
'''
Parameters:
n: number of documents
m: number of terms
k: number of topics
alpha: hyperparameter controlling distribution over topics
beta: hyperparameter controlling distribution over terms
z_dn: topic assignment matrix
n_mk: number of times term m is assigned topic k across all documents
n_dk: number of times topic k is assigned across document d
n_k: total number of times topic k is assigned across all documents
n_d: total number of words across document d
V_dk: term-topic matrix
z_dn[document][term]: topic assignment for term t in document d
'''
self.n = n
self.m = m
self.k = k
self.alpha = a
self.beta = b
self.z_dn = None
self.n_mk = None
self.n_dk = None
self.n_k = None
self.n_d = None
self.V_dk = None
<|repo_name|>zhaofengwen/PyCascades<|file_sep|>/pycascades/__init__.py
from .lda import *
from .ldamodel import *
from .sampling import *<|repo_name|>zhaofengwen/PyCascades<|file_sep|>/README.md
# PyCascades
PyCascades is an implementation of Gibbs sampling algorithm applied to Latent Dirichlet Allocation (LDA). The code is written entirely using Python language.
### Example
python
import pycascades as pc
documents_raw=['Human machine interface for lab abc computer applications','A survey of user opinion of computer system response time','The EPS user interface management system','System and human system engineering testing of EPS','Relation of user perceived response time to error measurement','The generation of random binary unordered trees','The intersection graph of paths in trees','Graph minors IV Widths of trees and well quasi ordering','Graph minors A survey']
stoplist=set('for a of the and to in'.split())
texts=[[word for word in document.lower().split() if word not in stoplist]for document in documents_raw]
from collections import defaultdict
frequency=defaultdict(int)
for text in texts:
frequency[word]+=1
texts=[[token for token in text if frequency[token]>1]for text in texts]
dictionary=gensim.corpora.Dictionary(texts)
corpus_bow=[dictionary.doc2bow(text)for text in texts]
gensim_ldamodel=LdaModel(corpus_bow,num_topics=10,id2word=dictionary)
pycascades_ldamodel_wrapper=GibbsSampler(n=len(corpus_bow),m=len(dictionary),k=10,alpha=.1,beta=.01)
pycascades_ldamodel_wrapper.initialize(corpus_bow,dictionary)
pycascades_ldamodel_wrapper.gibbs_sampling()
print('LDA model trained by Gensim package')
print(gensim_ldamodel.print_topics(num_topics=10,num_words=3))
print('LDA model trained by PyCascades package')
print(pycascades_ldamodel_wrapper.print_top_words(num_words=3))
### References
Blei D.M., Ng A.Y., Jordan M.I., "Latent Dirichlet Allocation", Journal of Machine Learning Research (2003), Volume 3(January), pp155-212.
Chang J., Blei D.M., "Document classification with latent dirichlet allocation", Advances In Neural Information Processing Systems (2009), pp1477-1484.
<|file_sep|># -*- coding: utf-8 -*-
"""
Created on Thu Dec 27 13:21:40 2018
@author: chengy
This script implements Gibbs sampling algorithm.
"""
from __future__ import print_function
import numpy as np
class GibbsSampler():
def __init__(self,n,m,k,a,b):
'''
Parameters:
n : number of documents
m : number of terms
k : number of topics
alpha : hyperparameter controlling distribution over topics
beta : hyperparameter controlling distribution over terms
'''
# initialize class variables
self.n=n
self.m=m
self.k=k
self.a=a
self.b=b
# initialize variables used by Gibbs sampling algorithm
# z_dn : topic assignment matrix
# n_mk : number of times term m is assigned topic k across all documents
# n_dk : number of times topic k is assigned across document d
# n_k : total number of times topic k is assigned across all documents
# n_d : total number of words across document d
# V_dk : term-topic matrix
# z_dn[document][term] : topic assignment for term t in document d
'''
z_dn=np.zeros((n,m,k))
n_mk=np.zeros((m,k))
n_dk=np.zeros((n,k))
n_k=np.zeros(k)
n_d=np.zeros(n)
V_dk=np.zeros((n,m,k))
'''
def initialize(self,corpus,dictionary):
'''
Initialize variables used by Gibbs sampling algorithm.
Parameters:
corpus : list containing BoW representations (lists)
for each document
dictionary : dictionary mapping from term ids (integers)
to terms (strings)
'''
n=len(corpus)
m=len(dictionary.keys())
k=self.k
alpha=self.a
beta=self.b
# initialize variables used by Gibbs sampling algorithm
z_dn=np.zeros((n,m,k))
n_mk=np.zeros((m,k))
n_dk=np.zeros((n,k))
n_k=np.zeros(k)
n_d=np.zeros(n)
V_dk=np.zeros((n,m,k))
# generate initial values
for i,d_i in enumerate(corpus):
N_i=len(d_i)
N_jks=np.random.multinomial(N_i,k)
N_jks+=1
N_js=np.sum(N_jks,axis=-1)+alpha
z_dijs=np.zeros(N_i,k)
jks=list(range(k))
js=list(range(N_i))
np.random.shuffle(jks)
np.random.shuffle(js)
jks_cycle=cycle(jks)
js_cycle=cycle(js)
js_index=dict(zip(js,np.arange(N_i)))
jks_index=dict(zip(jks,np.arange(k)))
for t_index,t_id_count_pair in enumerate(d_i):
t_id=t_id_count_pair[0]
t_count=t_id_count_pair[1]
j_index=tuple([js_index[t_index]for _in range(t_count)])
jks_index_list=[jks_index[next(jks_cycle)]for _in range(t_count)]
z_dijs[j_index,jks_index_list]=1
z_dn[i]=z_dijs
V_dijs=z_dijs/norm(z_dijs,axis=-1)[...,np.newaxis]
V_dijs[np.isnan(V_dijs)]=0
V_dk[i]=V_dijs
nk_js=np.sum(z_dijs,axis=-2)+alpha
nk_js=nk_js/norm(nk_js,axis=-1)[...,np.newaxis]
nk_js[np.isnan(nk_js)]=0
z_dn[i]=nk_js*z_dijs
z_dn[i]=z_dn[i]/norm(z_dn[i],axis=-1)[...,np.newaxis]
z_dn[i][np.isnan(z_dn[i])]=0
def gibbs_sampling(self,iters,sample_rate):
'''
Perform Gibbs sampling.
Parameters:
iters : number iterations
Returns:
None
'''
if sample_rate
