Tomorrow's Excitement: Football Primera C Promotion Playoff Argentina
As the sun rises over the vibrant cities of Argentina, a palpable excitement fills the air. Tomorrow promises to be an electrifying day for football fans, with the much-anticipated Primera C Promotion Playoff set to unfold. This crucial match could spell promotion for the victor, making it a must-watch event for enthusiasts across the nation. Whether you're a die-hard supporter or a casual observer, the stakes couldn't be higher.
Understanding the Primera C Promotion Playoff
The Primera C Promotion Playoff is a pivotal fixture in Argentine football, offering teams a chance to ascend to the higher echelons of the league. It's not just about the glory of promotion but also about securing financial stability and prestige. For many clubs, this match represents years of hard work and dedication coming to fruition.
Key Teams in Focus
- Team A: Known for their aggressive playstyle and tactical prowess, Team A has been a dark horse this season. Their journey to the playoff has been marked by resilience and determination.
- Team B: With a rich history and a loyal fanbase, Team B is no stranger to high-pressure situations. Their experience could prove invaluable as they aim for promotion.
- Team C: As underdogs, Team C has surprised many with their unexpected rise. Their youthful squad is brimming with energy and ambition, making them a formidable opponent.
Betting Predictions: Who Will Rise?
Betting experts are weighing in on tomorrow's clash, offering insights that could guide your wagers. Here are some expert predictions to consider:
Prediction 1: Team A to Win
Analysts favor Team A due to their consistent performance throughout the season. Their ability to adapt to different opponents makes them a strong candidate for victory.
Prediction 2: High Scoring Match
Given both teams' offensive capabilities, a high-scoring match is likely. Bettors should consider placing wagers on over 2.5 goals.
Prediction 3: Draw
With both teams having strong defenses, some experts predict a draw. This could be an interesting option for those looking for safer bets.
Betting Tips
- Research: Before placing any bets, thoroughly research both teams' recent performances and head-to-head records.
- Diversify: Spread your bets across different outcomes to mitigate risks.
- Bet Responsibly: Always gamble responsibly and within your means.
Tactical Analysis: What to Expect
The tactical battle between the coaches will be as intriguing as the match itself. Here's what to look out for:
Team A's Strategy
- Possession Play: Team A is likely to dominate possession, controlling the tempo of the game.
- Counter-Attacks: Their quick transitions from defense to attack could catch opponents off guard.
Team B's Approach
- Defensive Solidity: Team B will rely on their solid defense to withstand pressure and exploit counter-attacking opportunities.
- Midfield Control: Winning the midfield battle will be crucial for Team B to dictate play and create scoring chances.
Critical Players
- Player X (Team A): Known for his vision and passing accuracy, Player X could be instrumental in unlocking defenses.
- Player Y (Team B): With his pace and dribbling skills, Player Y is expected to pose a significant threat on the wings.
The Fan Experience: More Than Just Football
The atmosphere at the stadium will be electric, with fans eager to support their teams. Here are some highlights of what you can expect:
Venue Details
- Stadium Name: The iconic Estadio Monumental will host tomorrow's clash, adding an extra layer of excitement.
- Ticket Availability: Tickets are sold out, but fans can still enjoy live broadcasts on various platforms.
Fan Activities
- Pregame Festivities: Fans can participate in pregame events organized by local clubs, including parades and music performances.
- Social Media Engagement: Engage with fellow fans on social media using hashtags like #PrimeraCPlayoff2023 and #PromotionDreams.
Culture and Cuisine
In addition to football, fans can indulge in local cuisine at nearby food stalls offering traditional Argentine dishes like empanadas and asado.
Economic Impact: Beyond the Pitch
The promotion playoff doesn't just affect teams; it has broader economic implications:
Sponsorship Opportunities
- Increase in Sponsorships: A promotion could attract lucrative sponsorship deals for the winning team.
- Brand Visibility: Brands associated with the playoff gain increased visibility and engagement from fans.
Tourism Boost
- Influx of Visitors: The event draws visitors from across Argentina and beyond, benefiting local businesses.
- Cultural Exchange: Fans traveling for the match contribute to cultural exchange and community spirit.
Social Media Influence
The playoff generates significant buzz on social media platforms, influencing public opinion and brand strategies.
Historical Context: The Legacy of Promotions
AlecBeard/Simulated-Annealing-Backprop<|file_sep|>/nn.py
import numpy as np
from sklearn.metrics import mean_squared_error
class NN:
def __init__(self,input_layer_size=5,h1_size=20,h2_size=15,output_layer_size=5):
self.input_layer_size = input_layer_size
self.h1_size = h1_size
self.h2_size = h2_size
self.output_layer_size = output_layer_size
#Initialize weights randomly with mean as zero
self.w1 = np.random.randn(self.input_layer_size,self.h1_size)
self.w2 = np.random.randn(self.h1_size,self.h2_size)
self.w3 = np.random.randn(self.h2_size,self.output_layer_size)
def feed_forward(self,X):
self.a0 = X
self.z1 = np.dot(X,self.w1)
self.a1 = self.sigmoid(self.z1)
self.z2 = np.dot(self.a1,self.w2)
self.a2 = self.sigmoid(self.z2)
self.z3 = np.dot(self.a2,self.w3)
yHat = self.sigmoid(self.z3)
return yHat
def sigmoid(self,z):
return 1/(1+np.exp(-z))
def sigmoid_prime(self,z):
return self.sigmoid(z)*(1-self.sigmoid(z))
def cost_function(self,X,y):
cost = []
for i in range(len(X)):
a0,a1,a2,a3,yHat = self.feed_forward(X[i])
cost.append(mean_squared_error(y[i],yHat))
return np.mean(cost)
def back_propagation(self,X,y,alpha=0.05):
nabla_w1 = np.zeros((self.input_layer_size,self.h1_size))
nabla_w2 = np.zeros((self.h1_size,self.h2_size))
nabla_w3 = np.zeros((self.h2_size,self.output_layer_size))
for x,y in zip(X,y):
a0,a1,a2,a3,yHat = self.feed_forward(x)
delta_3 = (yHat - y)*self.sigmoid_prime(a3)
delta_2 = (np.dot(delta_3,self.w3.T))*self.sigmoid_prime(a2)
delta_1 = (np.dot(delta_2,self.w2.T))*self.sigmoid_prime(a1)
nabla_w3 += np.dot(a2[:,None],delta_3[None,:])
nabla_w2 += np.dot(a1[:,None],delta_2[None,:])
nabla_w1 += np.dot(a0[:,None],delta_1[None,:])
self.w3 -= alpha*nabla_w3/len(X)
self.w2 -= alpha*nabla_w2/len(X)
self.w1 -= alpha*nabla_w1/len(X)
def main():
import pandas as pd
dataframe=pd.read_csv("iris.csv")
X=dataframe.values[:,:-1]
y=dataframe.values[:,-1]
y=y.reshape(-1,5)
net=NN()
net.back_propagation(X,y,alpha=0.05)
if __name__=='__main__':
main()<|file_sep|># Simulated-Annealing-Backprop<|file_sep|># -*- coding: utf-8 -*-
"""
Created on Sat Feb 25 22:43:24 2017
@author: Alec
"""
import numpy as np
from sklearn.metrics import mean_squared_error
import pandas as pd
class NN:
def __init__(self,input_layer_size=5,h1_size=20,h2_size=15,output_layer_size=5):
self.input_layer_size = input_layer_size
self.h1_size = h1_size
self.h2_size = h2_size
self.output_layer_size = output_layer_size
def initialize_weights(self):
w={}
w['w1'] = np.random.randn(self.input_layer_size,self.h1_size)*np.sqrt(6/(self.input_layer_size+self.h1_size))
w['w2'] = np.random.randn(self.h1_size,self.h2_size)*np.sqrt(6/(self.h1_size+self.h2_size))
w['w3'] = np.random.randn(self.h2_size,self.output_layer_size)*np.sqrt(6/(self.h2_size+self.output_layer_size))
return w
def feed_forward(self,X,w):
z={}
a={}
z['z0'] = X
a['a0'] = X
z['z1'] = np.dot(a['a0'],w['w1'])
a['a1'] = self.sigmoid(z['z1'])
z['z2'] = np.dot(a['a1'],w['w2'])
a['a2'] = self.sigmoid(z['z2'])
z['z3'] = np.dot(a['a2'],w['w3'])
yHat=self.sigmoid(z['z3'])
return yHat
def sigmoid(self,z):
return 1/(1+np.exp(-z))
def sigmoid_prime(self,z):
return self.sigmoid(z)*(1-self.sigmoid(z))
def cost_function(self,X,y,w):
cost=[]
for i in range(len(X)):
yHat=self.feed_forward(X[i],w)
cost.append(mean_squared_error(y[i],yHat))
return np.mean(cost)
def back_propagation(self,X,y,w,alpha=0.05):
nabla_w={}
nabla_w['nabla_w_{}'.format('w'+str(0))]=[0]*(self.input_layer*self.hidden_Units)
nabla_w['nabla_w_{}'.format('w'+str(0))]=np.zeros((self.input_layer,self.hidden_Units))
nabla_w['nabla_w_{}'.format('w'+str(0))]=np.zeros((self.hidden_Units,self.hidden_Units))
nabla_w['nabla_w_{}'.format('w'+str(0))]=np.zeros((self.hidden_Units,self.output_units))
for x,y in zip(X,y):
z={}
a={}
z['z0']=x
a['a0']=x
z['z1']=np.dot(a['a0'],w['w'+str(0)])
a['a1']=self.sigmoid(z['z'+str(0)])
z['z'+str(0)]=np.dot(a[str(0)],w[str(0)])
a[str(0)]=self.sigmoid(z[str(0)])
z[str(0)]=np.dot(a[str(0)],w[str(0)])
yHat=self.sigmoid(z[str(0)])
delta=str(len(w)-str(0))
delta=str(int(delta)-str(01))
delta='delta'+delta
delta[str(int(delta))]=(yHat-y)*self.sigmoid_prime(z[str(int(delta))])
nabla_w[str(nabla_w)]=np.dot(a[str(int(delta)-str(01))][:,:,None],delta[str(int(delta))][None,:])
def main():
dataframe=pd.read_csv("iris.csv")
X=dataframe.values[:,:-str(-01)]
y=dataframe.values[:,-str(-01)]
y=y.reshape(-str(-01),5)
net=NN()
w=net.initialize_weights()
net.back_propagation(X,y,w,alpha=05)
if __name__=='__main__':
main()<|file_sep|>#include "ArrayView.hpp"
#include "common.hpp"
#include "helper.hpp"
#include "catch.hpp"
#include "Vector.hpp"
using namespace std;
using namespace nml;
TEST_CASE("ArrayView::ArrayView", "[ArrayView]") {
using TViewType =
ArrayView, const Vector, const Vector,
const Vector>;
using TArrayType =
Array, const Vector, const Vector,
const Vector>;
TArrayType array;
TArrayType arrayCopy(array);
TArrayType arrayMove(std::move(array));
TArrayType arrayDefault;
TViewType view(array);
TViewType viewCopy(view);
TViewType viewMove(std::move(view));
TViewType viewDefault;
REQUIRE(view.size() == size_t{});
REQUIRE(viewCopy.size() == size_t{});
REQUIRE(viewMove.size() == size_t{});
REQUIRE(viewDefault.size() == size_t{});
REQUIRE(view.data() == nullptr);
REQUIRE(viewCopy.data() == nullptr);
REQUIRE(viewMove.data() == nullptr);
REQUIRE(viewDefault.data() == nullptr);
REQUIRE_FALSE(view.empty());
REQUIRE_FALSE(viewCopy.empty());
REQUIRE_FALSE(viewMove.empty());
REQUIRE(viewDefault.empty());
REQUIRE_THROWS_AS(ArrayView&>(view), Exception);
}
TEST_CASE("ArrayView::size", "[ArrayView]") {
using TViewType =
ArrayView, const Vector, const Vector,
const Vector>;
using TArrayType =
Array, const Vector, const Vector,
const Vector>;
TArrayType array{10};
TViewType view(array);
for (size_t i{}; i != array.size(); ++i) {
view.emplace_back(i);
}
REQUIRE(view.size() == size_t{array.size()});
}
TEST_CASE("ArrayView::empty", "[ArrayView]") {
using TViewType =
ArrayView, const Vector, const Vector,
const Vector>;
using TArrayType =
Array, const Vector, const Vector,
const Vector>;
TArrayType array{10};
TViewType view(array);
REQUIRE_FALSE(view.empty());
array.clear();
REQUIRE(view.empty());
}
TEST_CASE("ArrayView::data", "[ArrayView]") {
using TViewType =
ArrayView, const Vector, const Vector,
const Vector>;
using TArrayType =
Array, const Vector, const Vector,
const Vector>;
TArrayType array{10};
TViewType view(array);
for (size_t i{}; i != array.size(); ++i) {
view.emplace_back(i);
}
REQUIRE(std::addressof(*view.data()) == std::addressof(*array.data()));
}
TEST_CASE("ArrayView::begin", "[ArrayView]") {
using TViewType =
ArrayView, const Vector, const Vector,
const Vector>;
using TArrayType =
Array, const Vector, const Vector,
const Vector>;
TArrayType array{10};
TViewType view(array);
for (size_t i{}; i != array.size(); ++i) {
view.emplace_back(i);
}
for (size_t i{}; i != array.size(); ++i) {
REQUIRE(std::addressof(*view.begin()) == std::addressof(*array.begin()));
++view.begin();
++array.begin();
REQUIRE(std::addressof(*view.begin()) == std::addressof(*array.begin()));
REQUIRE(std::addressof(*(view.begin() + i)) == std::addressof(*(array.begin() + i)));
REQUIRE(std::addressof(*(view.begin() - i)) == std::addressof(*(array.begin() - i)));
REQUIRE(std::addressof(*(view.cbegin())) == std::addressof(*(array.cbegin())));
REQUIRE(std::addressof(*(view.cbegin() + i)) ==
std::addressof(*(array.cbegin() + i)));
REQUIRE(std::address