Overview of Liga 2 Feminin Group 2 Romania
The Liga 2 Feminin Group 2 in Romania is a vibrant and competitive football league, showcasing some of the most promising talents in women's football. As we approach tomorrow's fixtures, fans and bettors alike are eagerly anticipating the matches that promise to deliver thrilling performances and unexpected outcomes. This article provides an in-depth analysis of the scheduled games, offering expert betting predictions and insights into each team's form and strategy.
Upcoming Matches for Tomorrow
- Team A vs. Team B
- Team C vs. Team D
- Team E vs. Team F
Match Analysis: Team A vs. Team B
The clash between Team A and Team B is expected to be a tightly contested affair. Both teams have shown remarkable resilience throughout the season, with Team A boasting a solid defensive record and Team B known for their aggressive attacking play. Key players to watch include Jane Doe from Team A, who has been instrumental in their defensive setup, and Anna Smith from Team B, whose goal-scoring prowess could be the difference-maker.
Betting Predictions
- 1X2 Prediction: Draw - Given both teams' strengths, a draw seems likely.
- Total Goals: Under 2.5 - Expect a low-scoring game with strategic defense playing a crucial role.
- Both Teams to Score: No - With strong defenses on both sides, goals might be hard to come by.
Tactical Insights
Team A is expected to adopt a conservative approach, focusing on maintaining their defensive solidity while looking for counter-attacking opportunities. On the other hand, Team B will likely push forward with their dynamic forward line, aiming to break through Team A's defense early in the game.
Key Players
- Jane Doe (Team A) - Known for her exceptional tackling and interceptions.
- Anne Smith (Team B) - A prolific striker with an eye for goal.
Past Performance
In their previous encounters, both teams have displayed evenly matched performances. However, Team A has a slight edge in recent matches, winning two out of their last three meetings against Team B.
Match Analysis: Team C vs. Team D
The match between Team C and Team D is set to be an exciting encounter. Team C has been on an impressive run of form, securing several victories in their last few matches. Meanwhile, Team D has struggled with consistency but possesses a talented squad capable of pulling off upsets.
Betting Predictions
- 1X2 Prediction: Team C to Win - Their current form suggests they are favorites.
- Total Goals: Over 2.5 - Expect an open game with plenty of scoring chances.
- Both Teams to Score: Yes - Both teams have potent attacking options.
Tactical Insights
Team C will likely dominate possession and control the midfield, using their technical skills to create scoring opportunities. Team D will need to focus on disrupting Team C's rhythm and capitalizing on counter-attacks.
Key Players
- Maria Johnson (Team C) - A creative midfielder known for her vision and passing ability.
- Laura Brown (Team D) - A versatile defender who can also contribute to attacks.
Past Performance
In their head-to-head history, both teams have had mixed results. However, Team C's recent form gives them the upper hand going into this match.
Match Analysis: Team E vs. Team F
The encounter between Team E and Team F promises to be a fascinating battle of styles. Team E is known for their disciplined defensive organization, while Team F relies on their fast-paced attacking play. This matchup could hinge on which team can impose their style more effectively.
Betting Predictions
- 1X2 Prediction: Draw - Both teams have strengths that could neutralize each other.
- Total Goals: Under 2.5 - Expect a tactical battle with limited scoring opportunities.
- Both Teams to Score: Yes - Despite defensive setups, both teams have players capable of breaking through.
Tactical Insights
Team E will aim to absorb pressure and hit on the break, using their speed in transition to catch Team F off guard. Conversely, Team F will look to press high and force errors from the opposition.
Key Players
- Sophia Green (Team E) - A reliable goalkeeper with excellent shot-stopping abilities.
- Natalie White (Team F) - An agile winger known for her dribbling skills and ability to create chances.
Past Performance
Historically, these teams have shared victories in their encounters. Their last match ended in a stalemate, reflecting the balanced nature of this fixture.
Betting Strategy Tips
<|repo_name|>mohammedabdullahmohamed/CNN-MNIST<|file_sep|>/README.md
# CNN-MNIST
This repository contains code written in Pytorch that uses CNNs for MNIST dataset
<|file_sep|># -*- coding: utf-8 -*-
"""
Created on Sat Aug 24 14:15:39 2019
@author: M
"""
import torch
import torchvision
from torch.autograd import Variable
import numpy as np
from torchvision import transforms
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
train_dataset = torchvision.datasets.MNIST(root='./data',
train=True,
transform=transforms.ToTensor(),
download=True)
test_dataset = torchvision.datasets.MNIST(root='./data',
train=False,
transform=transforms.ToTensor())
batch_size =100
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=batch_size,
shuffle=False)
class Net(nn.Module):
net = Net()
print(net)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(net.parameters(), lr=0.001)
def train(epoch):
# net.train()
running_loss =0
correct=0
total=0
for i , data in enumerate(train_loader):
images , labels = data
images = Variable(images)
labels = Variable(labels)
optimizer.zero_grad()
outputs = net(images)
loss = criterion(outputs , labels)
loss.backward()
optimizer.step()
running_loss +=loss.item()
_, predicted = torch.max(outputs.data ,1)
total +=labels.size(0)
correct +=(predicted ==labels).sum().item()
if i %10 ==9:
print('[%d , %5d] loss : %.3f'% (epoch+1,i+1 , running_loss/10))
running_loss=0
def test():
#net.eval()
correct=0
total=0
with torch.no_grad():
for data in test_loader:
images , labels = data
images = Variable(images)
labels = Variable(labels)
outputs = net(images)
_,predicted = torch.max(outputs.data ,1)
total +=labels.size(0)
correct +=(predicted ==labels).sum().item()
print('Accuracy : %.f %%' %(100*correct/total))
for epoch in range(10):
train(epoch)
test()<|file_sep|># -*- coding: utf-8 -*-
"""
Created on Sat Aug 24 15:57:59 2019
@author: M
"""
import torch
import torchvision
from torch.autograd import Variable
import numpy as np
from torchvision import transforms
import torch.nn as nn
import torch.nn.functional as F
train_dataset = torchvision.datasets.MNIST(root='./data',
train=True,
transform=transforms.ToTensor(),
download=True)
test_dataset = torchvision.datasets.MNIST(root='./data',
train=False,
transform=transforms.ToTensor())
batch_size =100
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=batch_size,
shuffle=False)
class Net(nn.Module):
net = Net()
print(net)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(net.parameters(), lr=0.001)
def train(epoch):
# net.train()
running_loss =0
correct=0
total=0
for i , data in enumerate(train_loader):
images , labels = data
images = Variable(images)
labels = Variable(labels)
optimizer.zero_grad()
outputs = net(images)
loss = criterion(outputs , labels)
loss.backward()
optimizer.step()
running_loss +=loss.item()
_, predicted = torch.max(outputs.data ,1)
total +=labels.size(0)
correct +=(predicted ==labels).sum().item()
if i %10 ==9:
print('[%d , %5d] loss : %.3f'% (epoch+1,i+1 , running_loss/10))
running_loss=0
def test():
#net.eval()
correct=0
total=0
with torch.no_grad():
for data in test_loader:
images , labels = data
images = Variable(images)
labels = Variable(labels)
outputs = net(images)
_,predicted = torch.max(outputs.data ,1)
total +=labels.size(0)
correct +=(predicted ==labels).sum().item()
print('Accuracy : %.f %%' %(100*correct/total))
for epoch in range(10):
train(epoch)
test()<|file_sep|>#pragma once
#include "ofMain.h"
// from https://github.com/bulletphysics/bullet3/blob/master/examples/pybullet/examples/userInput.py
class Player {
public:
ofVec3f pos;
ofVec3f velocity;
float angularVelocity;
float mass;
float radius;
Player(float m_, float r_): mass(m_), radius(r_) {
pos.set(0);
velocity.set(0);
angularVelocity=0;
}
void applyForce(ofVec3f force) {
ofVec3f deltaV(force/mass);
velocity+=deltaV;
}
void update() {
pos+=velocity;
}
};<|repo_name|>brunolemos/ofxBulletPhysics<|file_sep|>/src/ofxBulletPhysics.cpp
#include "ofxBulletPhysics.h"
#include "ofxBulletUtils.h"
void ofxBulletPhysics::setup(float timestep) {
timestep_=timestep;
broadphase_=new btDbvtBroadphase();
collisionConfiguration_=new btDefaultCollisionConfiguration();
dispatcher_=new btCollisionDispatcher(collisionConfiguration_);
solver_=new btSequentialImpulseConstraintSolver();
dynamicsWorld_=new btDiscreteDynamicsWorld(dispatcher_,broadphase_,solver_,collisionConfiguration_);
dynamicsWorld_->setGravity(btVector3(0,-10,0));
bodies_.clear();
forces_.clear();
}
void ofxBulletPhysics::update() {
dynamicsWorld_->stepSimulation(timestep_);
for (int i=bodies_.size()-1; i>=0; i--) {
if (!bodies_[i].valid()) {
delete bodies_[i].get();
bodies_.erase(bodies_.begin()+i);
forces_.erase(forces_.begin()+i);
continue;
}
btRigidBody* body=bodies_[i].get();
ofVec3f position=getPosition(body);
ofQuaternion rotation=getRotation(body);
if (forces_[i].force.length()>EPSILON || forces_[i].torque.length()>EPSILON) {
body->applyCentralForce(forces_[i].force);
body->applyTorque(forces_[i].torque);
forces_[i]=Force();
}
}
forceAccumulators_.clear();
}
void ofxBulletPhysics::draw() const {
for (int i=bodies_.size()-1; i>=0; i--) {
if (!bodies_[i].valid()) continue;
btRigidBody* body=bodies_[i].get();
ofPushMatrix();
ofVec3f position=getPosition(body);
ofQuaternion rotation=getRotation(body);
// cout << "position=" << position << endl;
// cout << "rotation=" << rotation.getAxis() << " " << rotation.getAngle()*180/M_PI << endl;
// ofSetColor(ofColor::orange);
// ofDrawSphere(position.x,body->getCollisionShape()->getLocalScaling().getY(),body->getCollisionShape()->getLocalScaling().getZ());
// ofDrawArrow(position.x,body->getCollisionShape()->getLocalScaling().getY(),body->getCollisionShape()->getLocalScaling().getZ(),position.x,body->getCollisionShape()->getLocalScaling().getY()+5,body->getCollisionShape()->getLocalScaling().getZ());
// ofSetColor(ofColor::green);
// ofPushMatrix();
// ofTranslate(position.x);
// ofRotate(rotation.getAngle()*180/M_PI,-rotation.getAxis().x,-rotation.getAxis().y,-rotation.getAxis().z);
// drawBox(body->getCollisionShape()->getAabb(),body->getCollisionShape()->getLocalScaling());
// ofPopMatrix();
// ofSetColor(ofColor::red);
// drawBox(getAabb(body),ofVec3f(1));
// drawBox(getAabb(body),ofVec3f(.01));
// drawBox(body->getCollisionShape()->getAabb(),ofVec3f(.01));
// ofSetColor(ofColor::white);
// cout << "mass=" << body->getInvMass() << endl;
// cout << "body=" << body << endl;
// cout << "shape=" << body->getCollisionShape() << endl;
// cout << "aabb.min=" << getAabb(body).minPt.x << ","<< getAabb(body).minPt.y<< ","<< getAabb(body).minPt.z<< endl;
// cout << "aabb.max=" << getAabb(body).maxPt.x << ","<< getAabb(body).maxPt.y<< ","<< getAabb(body).maxPt.z<< endl;
// cout << "shape.aabb.min=" << body->getCollisionShape()->getAabb().minPt.x<< ","<< body->getCollisionShape()->getAabb().minPt.y<< ","<< body->getCollisionShape()->getAabb().minPt.z<< endl;
// cout << "shape.aabb.max=" << body->getCollisionShape()->getAabb().maxPt.x<< ","<< body->getCollisionShape()->getAabb().maxPt.y<< ","<< body->getCollisionShape()->getAabb().maxPt.z<< endl;
// drawBox(getWorldAabb(body),ofVec3f(.01));
drawBody(position,body);
ofPopMatrix();
}
}
btRigidBody* ofxBulletPhysics::createBody(const btTransform& startTransform,const btCollisionShape* shape,btScalar mass,btVector3 localInertia) {
btDefaultMotionState* motionState=new btDefaultMotionState(startTransform);
btRigidBody::btRigidBodyConstructionInfo rbInfo(mass,motionState,shape,localInertia);
btRigidBody* body=new btRigidBody(rbInfo);
dynamicsWorld_->addRigidBody(body);
bodies_.push_back(shared_ptr(body));
forces_.push_back(Force());
return body;
}
void ofxBulletPhysics::destroyBody(btRigidBody* body) {
dynamicsWorld_->removeRigidBody(body);
for(int i=bodies_.size()-1; i>=0; i--) {
if(bodies_[i].compare(shared_ptr(body))) {
delete bodies_[i].release();
bodies_.erase(bodies_.begin()+i);
delete forces_[i].force.release();
forces_[i]=Force();
break;
}
}
}
btVector3 ofxBulletPhysics::getPosition(const btRigidBody* body) const {
btTransform trans;
body->getMotionState()->getWorldTransform(trans);
return btToOf(trans.getOrigin());
}
btQuaternion ofxBulletPhysics::getPosition(const btRigidBody* body,const btTransform& relativeTransform) const {
btTransform trans;
body->getMotionState()->getWorldTransform(trans);
btTransform combinedTrans(relativeTransform*trans);
return btToOf(combinedTrans.getRotation());
}
btVector3 ofxBulletPhysics::getPosition(const btRigidBody* body,const btVector3& relativePoint) const {
btTransform trans;
body->getMotionState()->getWorldTransform(trans);
