Boltzmann Machines
Contents
14. Boltzmann Machines¶
Why use a generative model rather than the more well known discriminative deep neural networks (DNN)?
Discriminitave methods have several limitations: They are mainly supervised learning methods, thus requiring labeled data. And there are tasks they cannot accomplish, like drawing new examples from an unknown probability distribution.
A generative model can learn to represent and sample from a probability distribution. The core idea is to learn a parametric model of the probability distribution from which the training data was drawn. As an example
a. A model for images could learn to draw new examples of cats and dogs, given a training dataset of images of cats and dogs.
b. Generate a sample of an ordered or disordered Ising model phase, having been given samples of such phases.
c. Model the trial function for Monte Carlo calculations
Both use gradient-descent based learning procedures for minimizing cost functions
Energy based models don’t use backpropagation and automatic differentiation for computing gradients, instead turning to Markov Chain Monte Carlo methods.
DNNs often have several hidden layers. A restricted Boltzmann machine has only one hidden layer, however several RBMs can be stacked to make up Deep Belief Networks, of which they constitute the building blocks.
History: The RBM was developed by amongst others Geoffrey Hinton, called by some the “Godfather of Deep Learning”, working with the University of Toronto and Google.
A BM is what we would call an undirected probabilistic graphical model with stochastic continuous or discrete units.
It is interpreted as a stochastic recurrent neural network where the state of each unit(neurons/nodes) depends on the units it is connected to. The weights in the network represent thus the strength of the interaction between various units/nodes.
It turns into a Hopfield network if we choose deterministic rather than stochastic units. In contrast to a Hopfield network, a BM is a so-called generative model. It allows us to generate new samples from the learned distribution.
A standard BM network is divided into a set of observable and visible units \(\hat{x}\) and a set of unknown hidden units/nodes \(\hat{h}\).
Additionally there can be bias nodes for the hidden and visible layers. These biases are normally set to \(1\).
BMs are stackable, meaning they cwe can train a BM which serves as input to another BM. We can construct deep networks for learning complex PDFs. The layers can be trained one after another, a feature which makes them popular in deep learning
However, they are often hard to train. This leads to the introduction of so-called restricted BMs, or RBMS. Here we take away all lateral connections between nodes in the visible layer as well as connections between nodes in the hidden layer. The network is illustrated in the figure below.
Figure 1:
14.1. The network¶
The network layers:
A function \(\mathbf{x}\) that represents the visible layer, a vector of \(M\) elements (nodes). This layer represents both what the RBM might be given as training input, and what we want it to be able to reconstruct. This might for example be the pixels of an image, the spin values of the Ising model, or coefficients representing speech.
The function \(\mathbf{h}\) represents the hidden, or latent, layer. A vector of \(N\) elements (nodes). Also called “feature detectors”.
The goal of the hidden layer is to increase the model’s expressive power. We encode complex interactions between visible variables by introducing additional, hidden variables that interact with visible degrees of freedom in a simple manner, yet still reproduce the complex correlations between visible degrees in the data once marginalized over (integrated out).
Examples of this trick being employed in physics:
The Hubbard-Stratonovich transformation
The introduction of ghost fields in gauge theory
Shadow wave functions in Quantum Monte Carlo simulations
The network parameters, to be optimized/learned:
\(\mathbf{a}\) represents the visible bias, a vector of same length as \(\mathbf{x}\).
\(\mathbf{b}\) represents the hidden bias, a vector of same lenght as \(\mathbf{h}\).
\(W\) represents the interaction weights, a matrix of size \(M\times N\).
14.1.1. Joint distribution¶
The restricted Boltzmann machine is described by a Bolztmann distribution
where \(Z\) is the normalization constant or partition function, defined as
It is common to ignore \(T_0\) by setting it to one.
14.1.2. Network Elements, the energy function¶
The function \(E(\mathbf{x},\mathbf{h})\) gives the energy of a configuration (pair of vectors) \((\mathbf{x}, \mathbf{h})\). The lower the energy of a configuration, the higher the probability of it. This function also depends on the parameters \(\mathbf{a}\), \(\mathbf{b}\) and \(W\). Thus, when we adjust them during the learning procedure, we are adjusting the energy function to best fit our problem.
14.1.3. Defining different types of RBMs¶
There are different variants of RBMs, and the differences lie in the types of visible and hidden units we choose as well as in the implementation of the energy function \(E(\mathbf{x},\mathbf{h})\). The connection between the nodes in the two layers is given by the weights \(w_{ij}\).
Binary-Binary RBM:
RBMs were first developed using binary units in both the visible and hidden layer. The corresponding energy function is defined as follows:
where the binary values taken on by the nodes are most commonly 0 and 1.
Gaussian-Binary RBM:
Another varient is the RBM where the visible units are Gaussian while the hidden units remain binary:
RBMs are Useful when we model continuous data (i.e., we wish \(\mathbf{x}\) to be continuous)
Requires a smaller learning rate, since there’s no upper bound to the value a component might take in the reconstruction
Other types of units include:
Softmax and multinomial units
Gaussian visible and hidden units
Binomial units
Rectified linear units
14.1.4. Cost function¶
When working with a training dataset, the most common training approach is maximizing the log-likelihood of the training data. The log likelihood characterizes the log-probability of generating the observed data using our generative model. Using this method our cost function is chosen as the negative log-likelihood. The learning then consists of trying to find parameters that maximize the probability of the dataset, and is known as Maximum Likelihood Estimation (MLE). Denoting the parameters as \(\boldsymbol{\theta} = a_1,...,a_M,b_1,...,b_N,w_{11},...,w_{MN}\), the log-likelihood is given by
where we used that the normalization constant does not depend on the data, \(\langle \text{log} Z(\{ \theta_i\}) \rangle = \text{log} Z(\{ \theta_i\})\) Our cost function is the negative log-likelihood, \(\mathcal{C}(\{ \theta_i \}) = - \mathcal{L}(\{ \theta_i \})\)
14.1.5. Optimization / Training¶
The training procedure of choice often is Stochastic Gradient Descent (SGD). It consists of a series of iterations where we update the parameters according to the equation
at each \(k\)-th iteration. There are a range of variants of the algorithm which aim at making the learning rate \(\eta\) more adaptive so the method might be more efficient while remaining stable.
We now need the gradient of the cost function in order to minimize it. We find that
where in order to simplify notation we defined the “operator”
and used the statistical mechanics relationship between expectation values and the log-partition function:
The data-dependent term in the gradient is known as the positive phase of the gradient, while the model-dependent term is known as the negative phase of the gradient. The aim of the training is to lower the energy of configurations that are near observed data points (increasing their probability), and raising the energy of configurations that are far from observed data points (decreasing their probability).
The gradient of the negative log-likelihood cost function of a Binary-Binary RBM is then
To get the expectation values with respect to the data, we set the visible units to each of the observed samples in the training data, then update the hidden units according to the conditional probability found before. We then average over all samples in the training data to calculate expectation values with respect to the data.
14.1.6. Kullback-Leibler relative entropy¶
When the goal of the training is to approximate a probability distribution, as it is in generative modeling, another relevant measure is the Kullback-Leibler divergence, also known as the relative entropy or Shannon entropy. It is a non-symmetric measure of the dissimilarity between two probability density functions \(p\) and \(q\). If \(p\) is the unkown probability which we approximate with \(q\), we can measure the difference by
Thus, the Kullback-Leibler divergence between the distribution of the training data \(f(\boldsymbol{x})\) and the model distribution \(p(\boldsymbol{x}| \boldsymbol{\theta})\) is
The first term is constant with respect to \(\boldsymbol{\theta}\) since \(f(\boldsymbol{x})\) is independent of \(\boldsymbol{\theta}\). Thus the Kullback-Leibler Divergence is minimal when the second term is minimal. The second term is the log-likelihood cost function, hence minimizing the Kullback-Leibler divergence is equivalent to maximizing the log-likelihood.
To further understand generative models it is useful to study the gradient of the cost function which is needed in order to minimize it using methods like stochastic gradient descent.
The partition function is the generating function of expectation values, in particular there are mathematical relationships between expectation values and the log-partition function. In this case we have
Here \(\langle \cdot \rangle_{model}\) is the expectation value over the model probability distribution \(p(\boldsymbol{x}| \boldsymbol{\theta})\).
14.2. Setting up for gradient descent calculations¶
Using the previous relationship we can express the gradient of the cost function as
This expression shows that the gradient of the log-likelihood cost function is a difference of moments, with one calculated from the data and one calculated from the model. The data-dependent term is called the positive phase and the model-dependent term is called the negative phase of the gradient. We see now that minimizing the cost function results in lowering the energy of configurations \(\boldsymbol{x}\) near points in the training data and increasing the energy of configurations not observed in the training data. That means we increase the model’s probability of configurations similar to those in the training data.
The gradient of the cost function also demonstrates why gradients of unsupervised, generative models must be computed differently from for those of for example FNNs. While the data-dependent expectation value is easily calculated based on the samples \(\boldsymbol{x}_i\) in the training data, we must sample from the model in order to generate samples from which to caclulate the model-dependent term. We sample from the model by using MCMC-based methods. We can not sample from the model directly because the partition function \(Z\) is generally intractable.
As in supervised machine learning problems, the goal is also here to perform well on unseen data, that is to have good generalization from the training data. The distribution \(f(x)\) we approximate is not the true distribution we wish to estimate, it is limited to the training data. Hence, in unsupervised training as well it is important to prevent overfitting to the training data. Thus it is common to add regularizers to the cost function in the same manner as we discussed for say linear regression.
14.3. RBMs for the quantum many body problem¶
The idea of applying RBMs to quantum many body problems was presented by G. Carleo and M. Troyer, working with ETH Zurich and Microsoft Research.
Some of their motivation included
The wave function \(\Psi\) is a monolithic mathematical quantity that contains all the information on a quantum state, be it a single particle or a complex molecule. In principle, an exponential amount of information is needed to fully encode a generic many-body quantum state.
There are still interesting open problems, including fundamental questions ranging from the dynamical properties of high-dimensional systems to the exact ground-state properties of strongly interacting fermions.
The difficulty lies in finding a general strategy to reduce the exponential complexity of the full many-body wave function down to its most essential features. That is
a. Dimensional reduction
b. Feature extraction
Among the most successful techniques to attack these challenges, artifical neural networks play a prominent role.
Want to understand whether an artifical neural network may adapt to describe a quantum system.
Carleo and Troyer applied the RBM to the quantum mechanical spin lattice systems of the Ising model and Heisenberg model, with encouraging results. Our goal is to test the method on systems of moving particles. For the spin lattice systems it was natural to use a binary-binary RBM, with the nodes taking values of 1 and -1. For moving particles, on the other hand, we want the visible nodes to be continuous, representing position coordinates. Thus, we start by choosing a Gaussian-binary RBM, where the visible nodes are continuous and hidden nodes take on values of 0 or 1. If eventually we would like the hidden nodes to be continuous as well the rectified linear units seem like the most relevant choice.
14.4. Representing the wave function¶
The wavefunction should be a probability amplitude depending on \(\boldsymbol{x}\). The RBM model is given by the joint distribution of \(\boldsymbol{x}\) and \(\boldsymbol{h}\)
To find the marginal distribution of \(\boldsymbol{x}\) we set:
Now this is what we use to represent the wave function, calling it a neural-network quantum state (NQS)
14.5. Choose the cost function¶
Now we don’t necessarily have training data (unless we generate it by using some other method). However, what we do have is the variational principle which allows us to obtain the ground state wave function by minimizing the expectation value of the energy of a trial wavefunction (corresponding to the untrained NQS). Similarly to the traditional variational Monte Carlo method then, it is the local energy we wish to minimize. The gradient to use for the stochastic gradient descent procedure is
where the local energy is given by
14.5.1. Mathematical details¶
Because we are restricted to potential functions which are positive it is convenient to express them as exponentials, so that
where \(E(\boldsymbol{x}_C)\) is called an energy function, and the exponential representation is the Boltzmann distribution. The joint distribution is defined as the product of potentials.
The joint distribution of the random variables is then
3 9
< < < ! ! M A T H _ B L O C K
with the partition function
\(T\) is a physics-inspired parameter named temperature and will be assumed to be 1 unless otherwise stated. The energy function of the Boltzmann machine determines the interactions between the nodes and is defined
Here \(\alpha_i^k (x_i)\) and \(\beta_j^l (h_j)\) are one-dimensional transfer functions or mappings from the given input value to the desired feature value. They can be arbitrary functions of the input variables and are independent of the parameterization (parameters referring to weight and biases), meaning they are not affected by training of the model. The indices \(k\) and \(l\) indicate that there can be multiple transfer functions per variable. Furthermore, \(a_i^k\) and \(b_j^l\) are the visible and hidden bias. \(w_{ij}^{kl}\) are weights of the \textbf{inter-layer} connection terms which connect visible and hidden units. \( v_{im}^k\) and \(u_{jn}^l\) are weights of the \textbf{intra-layer} connection terms which connect the visible units to each other and the hidden units to each other, respectively.
We remove the intra-layer connections by setting \(v_{im}\) and \(u_{jn}\) to zero. The expression for the energy of the RBM is then
resulting in
Similarly
Using Bayes theorem
Similarly
The original RBM had binary visible and hidden nodes. They were showned to be universal approximators of discrete distributions. It was also shown that adding hidden units yields strictly improved modelling power. The common choice of binary values are 0 and 1. However, in some physics applications, -1 and 1 might be a more natural choice. We will here use 0 and 1.
with the partition function
14.5.2. Marginal Probability Density Functions¶
In order to find the probability of any configuration of the visible units we derive the marginal probability density function.
A similar derivation yields the marginal probability of the hidden units
14.5.3. Conditional Probability Density Functions¶
We derive the probability of the hidden units given the visible units using Bayes’ rule
From this we find the probability of a hidden unit being “on” or “off”:
and
Similarly we have that the conditional probability of the visible units given the hidden are
14.5.4. Gaussian-Binary Restricted Boltzmann Machines¶
Inserting into the expression for \(E_{RBM}(\boldsymbol{x},\boldsymbol{h})\) in equation results in the energy
14.5.5. Joint Probability Density Function¶
with the partition function given by
14.5.6. Marginal Probability Density Functions¶
We proceed to find the marginal probability densitites of the Gaussian-binary RBM. We first marginalize over the binary hidden units to find \(p_{GB} (\boldsymbol{x})\)
We next marginalize over the visible units. This is the first time we marginalize over continuous values. We rewrite the exponential factor dependent on \(\boldsymbol{x}\) as a Gaussian function before we integrate in the last step.
14.5.7. Conditional Probability Density Functions¶
We finish by deriving the conditional probabilities.
The conditional probability of a binary hidden unit \(h_j\) being on or off again takes the form of a sigmoid function
The conditional probability of the continuous \(\boldsymbol{x}\) now has another form, however.
The form of these conditional probabilities explains the name “Gaussian” and the form of the Gaussian-binary energy function. We see that the conditional probability of \(x_i\) given \(\boldsymbol{h}\) is a normal distribution with mean \(b_i + \boldsymbol{w}_{i\ast}^T \boldsymbol{h}\) and variance \(\sigma_i^2\).
14.6. Neural Quantum States¶
The wavefunction should be a probability amplitude depending on \(\boldsymbol{x}\). The RBM model is given by the joint distribution of \(\boldsymbol{x}\) and \(\boldsymbol{h}\)
To find the marginal distribution of \(\boldsymbol{x}\) we set:
Now this is what we use to represent the wave function, calling it a neural-network quantum state (NQS)
The above wavefunction is the most general one because it allows for complex valued wavefunctions. However it fundamentally changes the probabilistic foundation of the RBM, because what is usually a probability in the RBM framework is now a an amplitude. This means that a lot of the theoretical framework usually used to interpret the model, i.e. graphical models, conditional probabilities, and Markov random fields, breaks down. If we assume the wavefunction to be postive definite, however, we can use the RBM to represent the squared wavefunction, and thereby a probability. This also makes it possible to sample from the model using Gibbs sampling, because we can obtain the conditional probabilities.
14.6.1. Cost function¶
This is where we deviate from what is common in machine learning. Rather than defining a cost function based on some dataset, our cost function is the energy of the quantum mechanical system. From the variational principle we know that minizing this energy should lead to the ground state wavefunction. As stated previously the local energy is given by
and the gradient is
where \(\alpha_i = a_1,...,a_M,b_1,...,b_N,w_{11},...,w_{MN}\).
We use that \(\frac{1}{\Psi}\frac{\partial \Psi}{\partial \alpha_i} = \frac{\partial \ln{\Psi}}{\partial \alpha_i}\), and find
This gives
If \(\Psi = \sqrt{F_{rbm}}\) we have
which results in
Let us assume again that our Hamiltonian is
where the first summation term represents the standard harmonic oscillator part and the latter the repulsive interaction between two electrons. Natural units (\(\hbar=c=e=m_e=1\)) are used, and \(P\) is the number of particles. This gives us the following expression for the local energy (\(D\) being the number of dimensions)
Letting each visible node in the Boltzmann machine represent one coordinate of one particle, we obtain
where we have that
We now have all the expressions neeeded to calculate the gradient of the expected local energy with respect to the RBM parameters \(\frac{\partial \langle E_L \rangle}{\partial \alpha_i}\).
If we use \(\Psi = \sqrt{F_{rbm}}\) we obtain
The difference between this equation and the previous one is that we multiply by a factor \(1/2\).
14.7. Python version for the two non-interacting particles¶
%matplotlib inline
# 2-electron VMC code for 2dim quantum dot with importance sampling
# Using gaussian rng for new positions and Metropolis- Hastings
# Added restricted boltzmann machine method for dealing with the wavefunction
# RBM code based heavily off of:
# https://github.com/CompPhysics/ComputationalPhysics2/tree/gh-pages/doc/Programs/BoltzmannMachines/MLcpp/src/CppCode/ob
from math import exp, sqrt
from random import random, seed, normalvariate
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import sys
# Trial wave function for the 2-electron quantum dot in two dims
def WaveFunction(r,a,b,w):
sigma=1.0
sig2 = sigma**2
Psi1 = 0.0
Psi2 = 1.0
Q = Qfac(r,b,w)
for iq in range(NumberParticles):
for ix in range(Dimension):
Psi1 += (r[iq,ix]-a[iq,ix])**2
for ih in range(NumberHidden):
Psi2 *= (1.0 + np.exp(Q[ih]))
Psi1 = np.exp(-Psi1/(2*sig2))
return Psi1*Psi2
# Local energy for the 2-electron quantum dot in two dims, using analytical local energy
def LocalEnergy(r,a,b,w):
sigma=1.0
sig2 = sigma**2
locenergy = 0.0
Q = Qfac(r,b,w)
for iq in range(NumberParticles):
for ix in range(Dimension):
sum1 = 0.0
sum2 = 0.0
for ih in range(NumberHidden):
sum1 += w[iq,ix,ih]/(1+np.exp(-Q[ih]))
sum2 += w[iq,ix,ih]**2 * np.exp(Q[ih]) / (1.0 + np.exp(Q[ih]))**2
dlnpsi1 = -(r[iq,ix] - a[iq,ix]) /sig2 + sum1/sig2
dlnpsi2 = -1/sig2 + sum2/sig2**2
locenergy += 0.5*(-dlnpsi1*dlnpsi1 - dlnpsi2 + r[iq,ix]**2)
if(interaction==True):
for iq1 in range(NumberParticles):
for iq2 in range(iq1):
distance = 0.0
for ix in range(Dimension):
distance += (r[iq1,ix] - r[iq2,ix])**2
locenergy += 1/sqrt(distance)
return locenergy
# Derivate of wave function ansatz as function of variational parameters
def DerivativeWFansatz(r,a,b,w):
sigma=1.0
sig2 = sigma**2
Q = Qfac(r,b,w)
WfDer = np.empty((3,),dtype=object)
WfDer = [np.copy(a),np.copy(b),np.copy(w)]
WfDer[0] = (r-a)/sig2
WfDer[1] = 1 / (1 + np.exp(-Q))
for ih in range(NumberHidden):
WfDer[2][:,:,ih] = w[:,:,ih] / (sig2*(1+np.exp(-Q[ih])))
return WfDer
# Setting up the quantum force for the two-electron quantum dot, recall that it is a vector
def QuantumForce(r,a,b,w):
sigma=1.0
sig2 = sigma**2
qforce = np.zeros((NumberParticles,Dimension), np.double)
sum1 = np.zeros((NumberParticles,Dimension), np.double)
Q = Qfac(r,b,w)
for ih in range(NumberHidden):
sum1 += w[:,:,ih]/(1+np.exp(-Q[ih]))
qforce = 2*(-(r-a)/sig2 + sum1/sig2)
return qforce
def Qfac(r,b,w):
Q = np.zeros((NumberHidden), np.double)
temp = np.zeros((NumberHidden), np.double)
for ih in range(NumberHidden):
temp[ih] = (r*w[:,:,ih]).sum()
Q = b + temp
return Q
# Computing the derivative of the energy and the energy
def EnergyMinimization(a,b,w):
NumberMCcycles= 10000
# Parameters in the Fokker-Planck simulation of the quantum force
D = 0.5
TimeStep = 0.05
# positions
PositionOld = np.zeros((NumberParticles,Dimension), np.double)
PositionNew = np.zeros((NumberParticles,Dimension), np.double)
# Quantum force
QuantumForceOld = np.zeros((NumberParticles,Dimension), np.double)
QuantumForceNew = np.zeros((NumberParticles,Dimension), np.double)
# seed for rng generator
seed()
energy = 0.0
DeltaE = 0.0
EnergyDer = np.empty((3,),dtype=object)
DeltaPsi = np.empty((3,),dtype=object)
DerivativePsiE = np.empty((3,),dtype=object)
EnergyDer = [np.copy(a),np.copy(b),np.copy(w)]
DeltaPsi = [np.copy(a),np.copy(b),np.copy(w)]
DerivativePsiE = [np.copy(a),np.copy(b),np.copy(w)]
for i in range(3): EnergyDer[i].fill(0.0)
for i in range(3): DeltaPsi[i].fill(0.0)
for i in range(3): DerivativePsiE[i].fill(0.0)
#Initial position
for i in range(NumberParticles):
for j in range(Dimension):
PositionOld[i,j] = normalvariate(0.0,1.0)*sqrt(TimeStep)
wfold = WaveFunction(PositionOld,a,b,w)
QuantumForceOld = QuantumForce(PositionOld,a,b,w)
#Loop over MC MCcycles
for MCcycle in range(NumberMCcycles):
#Trial position moving one particle at the time
for i in range(NumberParticles):
for j in range(Dimension):
PositionNew[i,j] = PositionOld[i,j]+normalvariate(0.0,1.0)*sqrt(TimeStep)+\
QuantumForceOld[i,j]*TimeStep*D
wfnew = WaveFunction(PositionNew,a,b,w)
QuantumForceNew = QuantumForce(PositionNew,a,b,w)
GreensFunction = 0.0
for j in range(Dimension):
GreensFunction += 0.5*(QuantumForceOld[i,j]+QuantumForceNew[i,j])*\
(D*TimeStep*0.5*(QuantumForceOld[i,j]-QuantumForceNew[i,j])-\
PositionNew[i,j]+PositionOld[i,j])
GreensFunction = exp(GreensFunction)
ProbabilityRatio = GreensFunction*wfnew**2/wfold**2
#Metropolis-Hastings test to see whether we accept the move
if random() <= ProbabilityRatio:
for j in range(Dimension):
PositionOld[i,j] = PositionNew[i,j]
QuantumForceOld[i,j] = QuantumForceNew[i,j]
wfold = wfnew
#print("wf new: ", wfnew)
#print("force on 1 new:", QuantumForceNew[0,:])
#print("pos of 1 new: ", PositionNew[0,:])
#print("force on 2 new:", QuantumForceNew[1,:])
#print("pos of 2 new: ", PositionNew[1,:])
DeltaE = LocalEnergy(PositionOld,a,b,w)
DerPsi = DerivativeWFansatz(PositionOld,a,b,w)
DeltaPsi[0] += DerPsi[0]
DeltaPsi[1] += DerPsi[1]
DeltaPsi[2] += DerPsi[2]
energy += DeltaE
DerivativePsiE[0] += DerPsi[0]*DeltaE
DerivativePsiE[1] += DerPsi[1]*DeltaE
DerivativePsiE[2] += DerPsi[2]*DeltaE
# We calculate mean values
energy /= NumberMCcycles
DerivativePsiE[0] /= NumberMCcycles
DerivativePsiE[1] /= NumberMCcycles
DerivativePsiE[2] /= NumberMCcycles
DeltaPsi[0] /= NumberMCcycles
DeltaPsi[1] /= NumberMCcycles
DeltaPsi[2] /= NumberMCcycles
EnergyDer[0] = 2*(DerivativePsiE[0]-DeltaPsi[0]*energy)
EnergyDer[1] = 2*(DerivativePsiE[1]-DeltaPsi[1]*energy)
EnergyDer[2] = 2*(DerivativePsiE[2]-DeltaPsi[2]*energy)
return energy, EnergyDer
#Here starts the main program with variable declarations
NumberParticles = 2
Dimension = 2
NumberHidden = 2
interaction=True
# guess for parameters
a=np.random.normal(loc=0.0, scale=0.001, size=(NumberParticles,Dimension))
b=np.random.normal(loc=0.0, scale=0.001, size=(NumberHidden))
w=np.random.normal(loc=0.0, scale=0.001, size=(NumberParticles,Dimension,NumberHidden))
# Set up iteration using stochastic gradient method
Energy = 0
EDerivative = np.empty((3,),dtype=object)
EDerivative = [np.copy(a),np.copy(b),np.copy(w)]
# Learning rate eta, max iterations, need to change to adaptive learning rate
eta = 0.001
MaxIterations = 50
iter = 0
np.seterr(invalid='raise')
Energies = np.zeros(MaxIterations)
EnergyDerivatives1 = np.zeros(MaxIterations)
EnergyDerivatives2 = np.zeros(MaxIterations)
while iter < MaxIterations:
Energy, EDerivative = EnergyMinimization(a,b,w)
agradient = EDerivative[0]
bgradient = EDerivative[1]
wgradient = EDerivative[2]
a -= eta*agradient
b -= eta*bgradient
w -= eta*wgradient
Energies[iter] = Energy
print("Energy:",Energy)
#EnergyDerivatives1[iter] = EDerivative[0]
#EnergyDerivatives2[iter] = EDerivative[1]
#EnergyDerivatives3[iter] = EDerivative[2]
iter += 1
#nice printout with Pandas
import pandas as pd
from pandas import DataFrame
pd.set_option('max_columns', 6)
data ={'Energy':Energies}#,'A Derivative':EnergyDerivatives1,'B Derivative':EnergyDerivatives2,'Weights Derivative':EnergyDerivatives3}
frame = pd.DataFrame(data)
print(frame)
Energy: 3.2051148702107777
Energy: 3.230940064679645
Energy: 3.3628297316891547
Energy: 3.379687729651322
Energy: 3.2412275649466284
Energy: 3.2232814980618887
Energy: 3.2602969984903907
Energy: 3.265829860950391
Energy: 3.281128578358681
Energy: 3.1892659690387117
Energy: 3.2633988850175215
Energy: 3.257814879097648
Energy: 3.251042998531674
Energy: 3.257917402278933
Energy: 3.2373537252132123
Energy: 3.222928809527423
Energy: 3.1896472468349044
Energy: 3.2701031898506714
Energy: 3.295788338799368
Energy: 3.255988211213934
Energy: 3.21812661100517
Energy: 3.2445977323761572
Energy: 3.244249315412173
Energy: 3.3364346819433734
Energy: 3.2457165876153096
Energy: 3.2605920300279703
Energy: 3.217079483769367
Energy: 3.2308021120975674
Energy: 3.294877896582793
---------------------------------------------------------------------------
KeyboardInterrupt Traceback (most recent call last)
Input In [1], in <cell line: 234>()
232 EnergyDerivatives2 = np.zeros(MaxIterations)
234 while iter < MaxIterations:
--> 235 Energy, EDerivative = EnergyMinimization(a,b,w)
236 agradient = EDerivative[0]
237 bgradient = EDerivative[1]
Input In [1], in EnergyMinimization(a, b, w)
158 for j in range(Dimension):
159 PositionNew[i,j] = PositionOld[i,j]+normalvariate(0.0,1.0)*sqrt(TimeStep)+\
160 QuantumForceOld[i,j]*TimeStep*D
--> 161 wfnew = WaveFunction(PositionNew,a,b,w)
162 QuantumForceNew = QuantumForce(PositionNew,a,b,w)
164 GreensFunction = 0.0
Input In [1], in WaveFunction(r, a, b, w)
23 Psi1 = 0.0
24 Psi2 = 1.0
---> 25 Q = Qfac(r,b,w)
27 for iq in range(NumberParticles):
28 for ix in range(Dimension):
Input In [1], in Qfac(r, b, w)
108 temp = np.zeros((NumberHidden), np.double)
110 for ih in range(NumberHidden):
--> 111 temp[ih] = (r*w[:,:,ih]).sum()
113 Q = b + temp
115 return Q
KeyboardInterrupt:


|
vending_machine
|
Implements the drivers of the LCD NHDC1264. More...
Go to the source code of this file.
Macros | |
| #define | LCD_A0 GPIO_PIN_5 |
| #define | COMP_LCD_CONFIG_VECTOR 11 /**Config vector length*/ |
| #define | LCD_ON 0xAF |
Functions | |
| void | LCDInitializeNHDC12864 (void) |
| LCD initialization. More... | |
| void | LCDWrite (char mode, char data) |
| Function to write data and commands in the LCD. More... | |
| void | LCDSetStartLine (char addr) |
| Function to read data rom the LCD. More... | |
| void | LCDSetColumn (char addr) |
| Function to set the column to write. More... | |
| void | LCDSetPage (char addr) |
| Function to set the page to write. More... | |
| void | LCDClear (void) |
| Function to clear the entire LCD. More... | |
| void | LCDReverseBlink (char nBlink) |
| Function to blink the LCD a number of times. More... | |
| void | LCDWriteChar7x8 (const char *alphabet, char letter) |
| Function to write a char in the LCD. More... | |
| void | LCDWriteString (const char *alphabet, char *str) |
| Function to write a string in the LCD. More... | |
Variables | |
| const char | times8 [95 *7] |
Implements the drivers of the LCD NHDC1264.
Module Name: LCD_NHDC12864.h
NHDC1264 Datasheet
Revision: 1.0 27-Nov-2018 Kleber Gouveia
Definition in file LCD_NHDC12864.h.
| #define COMP_LCD_CONFIG_VECTOR 11 /**Config vector length*/ |
LCD driver configurations
Definition at line 46 of file LCD_NHDC12864.h.
| #define LCD_A0 GPIO_PIN_5 |
LCD Pin mapping
Definition at line 33 of file LCD_NHDC12864.h.
| #define LCD_ON 0xAF |
LCD NHD-C12864B2Z commands
Definition at line 57 of file LCD_NHDC12864.h.
| void LCDClear | ( | void | ) |
Function to clear the entire LCD.
| none |
Definition at line 305 of file LCD_NHDC12864.c.
References LCDSetColumn(), and LCDSetPage().
Referenced by CLCD::clearScreen().
| void LCDInitializeNHDC12864 | ( | void | ) |
| void LCDReverseBlink | ( | char | nBlink | ) |
Function to blink the LCD a number of times.
| nBlink | - number of times to blink |
Definition at line 329 of file LCD_NHDC12864.c.
References LCDWrite().
| void LCDSetColumn | ( | char | addr | ) |
Function to set the column to write.
| addr | the position selected |
Definition at line 284 of file LCD_NHDC12864.c.
References LCDWrite().
Referenced by LCDClear(), LCDWriteString(), and CLCD::setPosition().
| void LCDSetPage | ( | char | addr | ) |
Function to set the page to write.
| addr | the position selected |
Definition at line 295 of file LCD_NHDC12864.c.
References LCDWrite().
Referenced by LCDClear(), LCDWriteString(), and CLCD::setPosition().
| void LCDSetStartLine | ( | char | addr | ) |
Function to read data rom the LCD.
| mode | can be DATA or COMM |
| addr | the position selected |
Definition at line 274 of file LCD_NHDC12864.c.
References LCDWrite().
| void LCDWrite | ( | char | mode, |
| char | data | ||
| ) |
Function to write data and commands in the LCD.
| mode | can be DATA or COMM |
| data | the information to be sent |
Definition at line 169 of file LCD_NHDC12864.c.
Referenced by LCDReverseBlink(), LCDSetColumn(), LCDSetPage(), LCDSetStartLine(), and LCDWriteChar7x8().
| void LCDWriteChar7x8 | ( | const char * | alphabet, |
| char | letter | ||
| ) |
Function to write a char in the LCD.
| *alphabet | - pointer to the font tipe selected |
| letter | - the char to write |
Definition at line 349 of file LCD_NHDC12864.c.
References LCDWrite().
Referenced by LCDWriteString().
| void LCDWriteString | ( | const char * | alphabet, |
| char * | str | ||
| ) |
Function to write a string in the LCD.
| *alphabet | - pointer to the font tipe selected |
| *str | - pointer to the string to write |
Definition at line 365 of file LCD_NHDC12864.c.
References LCDSetColumn(), LCDSetPage(), and LCDWriteChar7x8().
Referenced by CLCD::writeString().
| const char times8[95 *7] |
LCD display ON/OFF: ON
Lookup Table containing printed characteres 7x8 (between " " and "~"),Times New Human, ASCII.
Definition at line 45 of file LCD_NHDC12864.c.
Referenced by CLCD::writeString().


Length
Pages
0
0 printed
Scenes
0
Words
0
Characters
0
0 without spaces
Lines
0
without spaces
Duration
Total
0
Action
0
Dialogue
0


2 Kings 3:4
And Mesha king of Moab was a sheepmaster, and rendered unto the king of Israel an hundred thousand lambs, and an hundred thousand rams, with the wool.


{% extends "global/Page.html" %} {% load staticfiles otree_tags %} {% block title %} Resulting photo {% endblock %} {% block content %}
Would you like to delete this photo?


--- layout: post title: Exporting Custom Configurations From a Drupal 7 Site date: 2013-03-15 summary: After installing a Drupal7 site, I have made several configurations/customizations over time. Now I would like to export them somehow, in order to load them to another site. But the problem is that I don't remember exactly what I have done. The Features module does not help because you have to tell it exactly what to export, but I don't remember all the changes. tags: [Drupal] ---
After installing a Drupal7 site, I have made several configurations/customizations over time. Now I would like to export them somehow, in order to load them to another site. But the problem is that I don't remember exactly what I have done. The Features module does not help because you have to tell it exactly what to export, but I don't remember all the changes.
This is a difficult problem on Drupal 7, which is expected to be solved on Drupal 8, by the Config Management Initiative. However there is the Configuration Module that tries to solve the configuration problem on Drupal 7, as a backport feature from Drupal 8.
With the help of the Configuration module you basically track certain elements of configuration on your site (this bit is relatively similar to Features), and then it tells you when there are differences between what is stored in the filesystem and what is stored in the database (referred to as ActiveStore and DataStore). These config files can then be versioned and deployed. If the Diff module is installed, you can also see what has been changed with regards to ActiveStore vs DataStore.
This is useful for keeping track of specific configurations on a Drupal site, provides the ability to move these configurations between different environments (local, dev, qa, prod), and also allows to move configurations between completely different sites (migrate configurations).
This module helped me to solve my problem, although the solution was not 100% automatic and required some manual work. The basic steps are these:
Below is described in more details how to accomplish some of these steps.
These modules are installed on both Drupal sites. Doing it through the admin GUI takes a lot of time, and in my server very often the web server times out, so I have to do it with drush on command line, no matter whether I like it or not (actually I like it).
cd sites/all/modules/ wget http://ftp.drupal.org/files/projects/configuration-7.x-2.x-dev.tar.gz tar xfz configuration-7.x-2.x-dev.tar.gz drush --yes pm-enable configuration configuration_ui cd ../../ sudo mkdir sites/default/files/config sudo chown www-data: sites/default/files/config cd sites/all/modules/ wget http://ftp.drupal.org/files/projects/diff-7.x-3.2.tar.gz tar xfz diff-7.x-3.2.tar.gz drush --yes pm-enable diff drush php-eval "menu_rebuild();"
This is also done on both sites. Again I prefer to do it from the command line.
drush cc drush drush config-start-tracking --all drush config-export --all
After this, the directory sites/default/files/config/
will contain
all the exported configurations of the corresponding site. The
ActiveStore (DB) and the DataStore (config/
directory) will have
the same configuration.
This can be done maybe using Export and Import from the GUI. However
it is much more easy to just copy the directories from the command
line. We can move the config/
directory of the old site to
config_bak/
(or something like this), and in its place we copy the
config/ directory of the new site.
After this, the ActiveStore (DB) and the DataStore (config/
dir)
will have different contents. Comparing them from the UI of the
Configuration module is more easy and reliable that using any other
tricks (for example from command line).
On the path admin/config/system/configuration
we will find all the
configurations that are being tracked. Since we have changed the
DataStore from the command line, the status of each configuration
will be 'Processing…' for some time. After all the processing is
done, the status will show us what configurations are the same and
what are different with the ActiveStore.
Now we simply select and stop tracking all the configurations that have not been changed. We should also stop tracking some of the configurations that are not relevant or that contain any sensitive data (this part is a bit manual and tedious, since we have to check all the configurations one by one). In the end, we can export the remaining configurations and import them on another site.


|
Oracle® Database JDBC
Java API Reference 11g Release 2 E13995-02 | ||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object
oracle.jdbc.driver.OracleDriver
oracle.jdbc.OracleDriver
public class OracleDriver
The Oracle JDBC driver class that implements the java.sql.Driver
interface.
To access a database from a Java application, you must first provide the code
to register your installed driver with your program. You do this with the static
registerDriver()
method of the java.sql.DriverManager
class. This class provides a basic service for managing a set of JDBC drivers.
The registerDriver()
method takes as input a "driver" class, that
is, a class that implements the java.sql.Driver
interface, as is
the case with OracleDriver
.
Note: Alternatively, you can use the forName()
method of
the java.lang.Class
class to load the JDBC drivers directly. For
example: Class.forName ("oracle.jdbc.OracleDriver");
. However, this
method is valid only for JDK-compliant Java virtual machines. It is not valid
for Microsoft Java virtual machines.
You register the driver only once in your Java application.
DriverManager.registerDriver (new oracle.jdbc.OracleDriver());
Once you have registered the driver, you can open a connection to the
database with the static getConnection()
method of the
java.sql.DriverManager
class. The type of the object returned is
java.sql.Connection
.
The following signature takes the URL, user name, and password as separate parameters:
getConnection(String URL, String user, String password);
Where the URL is of the form:
jdbc:oracle:<drivertype>:@<database>
The following example connects user scott
with password
tiger
to a database with SID orcl
through port 1521 of
host myhost
, using the Thin driver.
Connection conn = DriverManager.getConnection
("jdbc:oracle:thin:@myhost:1521:orcl", "scott", "tiger");
The following signature takes the URL, user name, and password all as part of a URL parameter:
getConnection(String URL);
Where the URL is of the form:
jdbc:oracle:<drivertype>:<user>/<password>@<database>
The following example connects user scott
with password
tiger
to a database on host myhost
using the OCI
driver. In this case, however, the URL includes the userid and password, and is
the only input parameter.
Connection conn = DriverManager.getConnection
("jdbc:oracle:oci8:scott/tiger@myhost);
If you want to connect with the Thin driver, you must specify the port number
and SID. For example, if you want to connect to the database on host
myhost
that has a TCP/IP listener up on port 1521, and the
SID
(system identifier) is orcl
:
Connection conn = DriverManager.getConnection
("jdbc:oracle:thin:scott/tiger@myhost:1521:orcl);
The following signature takes a URL, together with a properties object that specifies user name and password (perhaps among other things):
getConnection(String URL, Properties info);
Where the URL
is of the form:
jdbc:oracle:<drivertype>:@<database>
In addition to the URL, use an object of the standard Java
Properties
class as input. For example:
java.util.Properties info = new java.util.Properties();
"password",
info.put ("user", "scott");
info.put
("tiger"
);
info.put
("defaultRowPrefetch","15");
getConnection
("jdbc:oracle:oci8:@",info);
The table below lists the connection properties that Oracle JDBC drivers support.
Connection Properties Recognized by Oracle JDBC Drivers
| Name | Short Name | Type | Description |
|---|---|---|---|
| user | n/a | String | the user name for logging into the database |
| password | n/a | String | the password for logging into the database |
| database | server | String | the connect string for the database |
| internal_logon | n/a | String | a role, such as sysdba or sysoper , that
allows you to log on as sys |
| defaultRowPrefetch | prefetch | String (containing integer value) | the default number of rows to prefetch from the server (default value is "10") |
| remarksReporting | remarks | String (containing boolean value) | "true" if getTables() and getColumns() should report TABLE_REMARKS; equivalent to using setRemarksReporting() (default value is "false") |
| defaultBatchValue | batchvalue | String (containing integer value) | the default batch value that triggers an execution request (default value is "10") |
| includeSynonyms | synonyms | String (containing boolean value) | "true" to include column information from predefined "synonym" SQL
entities when you execute a DataBaseMetaData getColumns()
call; equivalent to connection setIncludeSynonyms() call
(default value is "false") |
| processEscapes | n/a | String (containing boolean value) | "false" to disable escape processing for statements (Statement or
PreparedStatement) created from this connection. Set this to "false" if
you want to avoid many calls to
Statement.setEscapeProcessing(false); . This is espcially
usefull for PreparedStatement where a call to
setEscapeProcessing(false) would have no effect. The default
is "true". |
| defaultNChar | n/a | String (containing boolean value) | "false" is the default. If set to "true", the default behavior for
handling character datatypes is changed so that NCHAR/NVARCHAR2 become the
default. This means that setFormOfUse() won't be needed anymore when using
NCHAR/NVARCHAR2. This can also be set as a java property :java
-Doracle.jdbc.defaultNChar=true myApplication |
| useFetchSizeWithLongColumn | n/a | String (containing boolean value) | "false" is the default.  IT SHOULD NOT BE USED WITH ANY OTHER DRIVERS. If set to "true", the performance when retrieving data in a 'SELECT' will be improved but the default behavior for handling LONG columns will be changed to fetch multiple rows (prefetch size). It means that enough memory will be allocated to read this data. So if you want to use this property, make sure that the LONG columns you are retrieving are not too big or you may run out of memory. This property can also be set as a java property : java -Doracle.jdbc.useFetchSizeWithLongColumn=true
myApplication |
| SetFloatAndDoubleUseBinary | n/a | String (containing boolean value) | "false" is the default. If set to "true", causes the java.sql.PreparedStatment setFloat and setDouble API's to use internal binary format as for BINARY_FLOAT and BINARY_DOUBLE parameters. See oracle.jdbc.OraclePreparedStatement setBinaryFloat and setBinaryDouble |
Oracle provides four types of JDBC driver.
thin
. To connect user scott
with password
tiger
to a database with SID
(system identifier)
orcl
through port 1521 of host myhost
, using the
Thin driver, you would write :
Connection conn = DriverManager.getConnection
("jdbc:oracle:thin:@myhost:1521:orcl", "scott", "tiger");
oci
. To connect user scott
with password tiger
to a database with SID
(system
identifier) orcl
through port 1521 of host myhost
,
using the OCI driver, you would write :
Connection conn = DriverManager.getConnection
("jdbc:oracle:oci:@myhost:1521:orcl", "scott", "tiger");
Note that you can also specify the database by a TNSNAMES
entry.
You can find the available TNSNAMES
entries listed in the file
tnsnames.ora
on the client computer from which you are
connecting. For example, if you want to connect to the database on host
myhost
as user scott
with password
tiger
that has a TNSNAMES
entry of
MyHostString
, enter:
Connection conn = DriverManager.getConnection
("jdbc:oracle:oci8:@MyHostString","scott","tiger");
If your JDBC client and Oracle server are running on the same machine, the OCI
driver can use IPC (InterProcess Communication) to connect to the database
instead of a network connection. An IPC connection is much faster than a
network connection.
Connection conn = DriverManager.getConnection
("jdbc:oracle:oci8:@","scott","tiger");
thin
and there is no difference
in your code between using the Thin driver from a client application or from
inside a server.kprb
and it actually runs within a
default session. You are already "connected". Therefore the connection
should never be closed.You can also use the Oracle-specific defaultConnection() method of the OracleDriver class which is generally recommended:DriverManager.getConnection("jdbc:oracle:kprb:");
or:DriverManager.getConnection("jdbc:default:connection:");
OracleDriver ora = new OracleDriver();
Connection conn = ora.defaultConnection();
Note:
You are no longer required to register the OracleDriver
class for
connecting with the Server-Side Internal driver, although there is no harm in
doing so. This is true whether you are using getConnection()
or
defaultConnection()
to make the connection.DriverManager.getConnection()
method
returns a new Java Connection
object every time you call it. Note
that although the method is not creating a new physical connection (only a
single implicit connection is used), it is returning a new object.| Field Summary | |
|---|---|
static java.lang.String |
BUILD_DATE |
static boolean |
PRIVATE_TRACE |
static boolean |
TRACE |
| Constructor Summary | |
|---|---|
OracleDriver() |
| Method Summary | |
|---|---|
static java.lang.String |
getDriverVersion() Returns a String that specifies the Oracle version number of the driver. |
static java.lang.String |
getJDBCVersion() Returns a String that specifies the version of the JDBC spec supporte by the driver. |
static boolean |
isDebug() Returns true if this jar includes debug code. |
static boolean |
isDMS() Returns true if this jar includes DMS instrumentaion. |
static boolean |
isInServer() Returns true if this jar was built to run in the Oracle Java VM. |
static boolean |
isJDK14() Deprecated. |
static boolean |
isPrivateDebug() Returns true if this jar includes Oracle internal debug code. |
static void |
main(java.lang.String[] args) Prints a description of the Oracle JDBC driver .jar file to System.out. |
| Methods inherited from class java.lang.Object |
|---|
equals, getClass, hashCode, notify, notifyAll, toString, wait,
wait, wait |
| Field Detail |
|---|
public static final java.lang.String BUILD_DATE
public static final boolean TRACE
public static final boolean PRIVATE_TRACE
| Constructor Detail |
|---|
public OracleDriver()
| Method Detail |
|---|
public static final boolean isDMS()
public static final boolean isInServer()
public static final boolean isJDK14()
public static final boolean isDebug()
public static final boolean isPrivateDebug()
public static final java.lang.String getJDBCVersion()
public static final java.lang.String getDriverVersion()
public static void main(java.lang.String[] args)
args
- Ignored
|
Oracle® Database JDBC
Java API Reference 11g Release 2 E13995-02 | ||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


convlab.env package¶
Submodules¶
convlab.env.base module¶
-
class
convlab.env.base.
BaseEnv
(spec, e=None)[source]¶ Bases:
abc.ABC
The base Env class with API and helper methods. Use this to implement your env class that is compatible with the Lab APIs
e.g. env_spec “env”: [{
“name”: “PongNoFrameskip-v4”, “frame_op”: “concat”, “frame_op_len”: 4, “normalize_state”: false, “reward_scale”: “sign”, “num_envs”: 8, “max_t”: null, “max_frame”: 1e7}],
convlab.env.movie module¶
-
class
convlab.env.movie.
KBHelper
(movie_dictionary)[source]¶ Bases:
object
An assistant to fill in values for the agent (which knows about slots of values)
-
available_results_from_kb
(current_slots)[source]¶ Return the available movies in the movie_kb based on the current constraints
-
available_results_from_kb_for_slots
(inform_slots)[source]¶ Return the count statistics for each constraint in inform_slots
-
available_slot_values
(slot, kb_results)[source]¶ Return the set of values available for the slot based on the current constraints
-
database_results_for_agent
(current_slots)[source]¶ A dictionary of the number of results matching each current constraint. The agent needs this to decide what to do next.
-
fill_inform_slots
(inform_slots_to_be_filled, current_slots)[source]¶ Takes unfilled inform slots and current_slots, returns dictionary of filled informed slots (with values)
Arguments: inform_slots_to_be_filled – Something that looks like {starttime:None, theater:None} where starttime and theater are slots that the agent needs filled current_slots – Contains a record of all filled slots in the conversation so far - for now, just use current_slots[‘inform_slots’] which is a dictionary of the already filled-in slots
Returns: filled_in_slots – A dictionary of form {slot1:value1, slot2:value2} for each sloti in inform_slots_to_be_filled
-
-
class
convlab.env.movie.
MovieActInActOutEnvironment
(worker_id=None)[source]¶ Bases:
object
-
initialize_episode
()[source]¶ Initialize a new episode. This function is called every time a new episode is run.
-
reward_function
(dialog_status)[source]¶ Reward Function 1: a reward function based on the dialog_status
-
-
class
convlab.env.movie.
MovieEnv
(spec, e=None, env_space=None)[source]¶ Bases:
convlab.env.base.BaseEnv
Wrapper for Unity ML-Agents env to work with the Lab.
e.g. env_spec “env”: [{
“name”: “gridworld”, “max_t”: 20, “max_tick”: 3, “unity”: {
“gridSize”: 6, “numObstacles”: 2, “numGoals”: 1}
}],
-
patch_gym_spaces
(u_env)[source]¶ For standardization, use gym spaces to represent observation and action spaces. This method iterates through the multiple brains (multiagent) then constructs and returns lists of observation_spaces and action_spaces
-
-
class
convlab.env.movie.
RuleSimulator
(movie_dict=None, act_set=None, slot_set=None, start_set=None, params=None)[source]¶ Bases:
convlab.env.movie.UserSimulator
A rule-based user simulator for testing dialog policy
-
corrupt
(user_action)[source]¶ Randomly corrupt an action with error probs (slot_err_probability and slot_err_mode) on Slot and Intent (intent_err_probability).
-
-
class
convlab.env.movie.
StateTracker
(act_set, slot_set, movie_dictionary)[source]¶ Bases:
object
The state tracker maintains a record of which request slots are filled and which inform slots are filled
-
dialog_history_dictionaries
()[source]¶ Return the dictionary representation of the dialog history (includes values)
-
dialog_history_vectors
()[source]¶ Return the dialog history (both user and agent actions) in vector representation
-
initialize_episode
()[source]¶ Initialize a new episode (dialog), flush the current state and tracked slots
-
convlab.env.multiwoz module¶
-
class
convlab.env.multiwoz.
MultiWozEnv
(spec, e=None)[source]¶ Bases:
convlab.env.base.BaseEnv
Wrapper for Unity ML-Agents env to work with the Lab.
e.g. env_spec “env”: [{
“name”: “gridworld”, “max_t”: 20, “max_tick”: 3, “unity”: {
“gridSize”: 6, “numObstacles”: 2, “numGoals”: 1}
}],
Module contents¶
The environment module Contains graduated components from experiments for building/using environment. Provides the rich experience for agent embodiment, reflects the curriculum and allows teaching (possibly allows teacher to enter). To be designed by human and evolution module, based on the curriculum and fitness metrics.
-
class
convlab.env.
EnvSpace
(spec, aeb_space)[source]¶ Bases:
object
Subspace of AEBSpace, collection of all envs, with interface to Session logic; same methods as singleton envs. Access AgentSpace properties by: AgentSpace - AEBSpace - EnvSpace - Envs


class PayjpTokenParam
class PayjpCardForm
instance.
You must call Payjp.init(configuration)
first.class Token
.class Token
.PayjpCardForm.start
.
Use this at Activity.onActivityResult in the activity that you call PayjpCardForm.start
.class Token
.class Token
.interface Task
cancel
was called.null
class PayjpCardFormAbstractFragment
.android.view.inputmethod.EditorInfo#IME_ACTION_DONE
. params is from TextView.OnEditorActionListener#onEditorAction(TextView, int, KeyEvent)
.class PayjpCardFormResult
class PayjpThreeDSecureResult
enum CardBrand
logo.
You can use this to display card acceptance.
It will only show logos which provided by PayjpAcceptedBrandsView.setAcceptedBrands
.enum CardBrand
logo.
You can use this to display card acceptance.
It will only show logos which provided by PayjpAcceptedBrandsView.setAcceptedBrands
.class Token
.class PayjpCardFormResult
class PayjpThreeDSecureResult
class CardFormStatus.Complete
, the activity will finish.
But return class CardFormStatus.Error
, the activity will not finish and show error message.
The invocation execute in background.PayjpTokenBackgroundHandler.handleTokenInBackground
.
If the card token send to server successfully, status may be class CardFormStatus.Complete
,
and the Card form will finish and return the token.
If an error happened in sending to server, status may be class CardFormStatus.Error
,
and the Card form will not finish immediately.class PayjpTokenParam
payjp-android-cardio
.class Token
.Retrofit.Call
.interface Task
interface PayjpTokenService
instance.
You must call Payjp.init(configuration)
first.class Token
.interface Task
.class PayjpVerifier
instance.
You must call Payjp.init(configuration)
first.


freeCodeCamp Survey Form
Thank you for taking the time to help us improve the platform
Name
Email
Age
(Optional)
Which option best describes your current role?
Select current role
Student
Full time Job
Full time learner
Prefered not to say
Other
Would you recommend freeCodeCamp to a friend?
Definitely
Maybe
Not Sure
What is your favorite feature of freeCodeCamp?
Select an option
Challenges
Projects
Community
Open Source
What would you like to see improved?
(Check all that apply)
Front-end Projects
Back-end Projects
Data Visualization
Challenges
Open Source Community
Gitter help rooms
Videos
City Meetups
Wiki
Forum
Additional Courses
Any comments or suggestions?
Submit


JJShine® has been manufacturing high quality stainless steel kitchen sinks for over three decades and has been improving millions of kitchens across India and overseas, one sink at a time.
Find Out MoreWe have one of the widest ranges of stainless steel sinks to match any theme that you may have in mind. All of our sinks are manufactured from the highest quality materials while paying close attention to the most minute of details. Each JJShine® sink is carefully crafted to satisfy our sophisticated customers that demand the very best. We at JJShine® strongly believe that our high quality sinks make your life in the kitchen easier and more enjoyable while providing long lasting beauty and value for years to come.
Get Started!Ready to start using JJShine® sinks? That's great! Give us a call or send us an email and we will get back to you as soon as possible!
997-119-0844


Esteemed reader,
1This is a manifesto about the importance of numbering paragraphs. It is part of a markdown-it plugin for rendering automatic paragraph numbering in markdown texts, and it also functions as part of the test suite for that plugin.[1]
1.0.1Written texts are created to convey an author’s ideas to a reader. Generally a written text contains ideas organized around a theme, and that theme may be broken down into smaller pieces: volumes, sections, chapters, paragraphs, sentences and words.
1.0.2Words and sentences are the primary repositories of meaning, but paragraphs are most basic element for conveying a complete idea[2]. With proper usage, each paragraph contains an idea that is discrete and atomic: discrete in that the author believed the idea could be properly understood without reference to information not previously provided, and atomic in that the author did not believe the idea could reasonably be broken down into smaller ideas. A paragraph is as large as it needs to be and as small as it can be in order to convey a discrete, atomic idea.
2.0.1Paragraph numbering is one of several schemes of reference for text, including page number, line number, section, and complete work. The aim of this manifesto is to convince everyone that in many cases paragraph numbering is more beneficial than any other scheme, and that therefore we should always support this scheme— by numbering paragraphs in written works.
2.0.2Reference schemes are important for two reasons, which can be summarized as follows:
2.0.31. Comprehension: references form a scaffold for comprehension that helps a reader to understand and remember a text.
2.0.42. Connection: references provide points of consensus for sharing texts with other people.
2.0.5All of the reference schemes listed above fulfil these two functions, and they benefit individual and collective understanding to the extent that they effectively balance competing concerns:
- being specific - being concise - being portable - being meaningful
2.0.6The following sections detail each of the reference schemes mentioned above, giving a few examples of each, exploring cases in which each may have utility, and rating each for specificity, conciseness, portability, and meaningfulness. [3]
2.1.1- All citations in most news stories, e.g. “A recent study...” - All citations in most social media posts - Most hyperlinks - Citations for paraphrased ideas in academic writing (APA) - Citations of auditory works, e.g. podcast episodes - Citations of stories in oral traditions?
2.1.2Specificity: 1
It’s hard to get less specific than citing a whole work.
Perhaps one could accomplish this by citing an author without
specifying a work, e.g. “Thomas Jefferson once wrote that...”
2.1.3Conciseness: 10
Every reference must contain the full work, so there is no
reasonable way to obtain a more concise reference.
2.1.4Portability: 7
Citing a complete work is quite portable, although some issues
arise when a work has multiple editions. For example, “the Bible”
is sometimes cited without reference to a particular translation.
2.1.5Meaningfulness: 3
For sharing information, it is quite meaningful to know the work
from which an idea came. However, it is also quite possible to
convey a lot more meaning in a reference when collaborating.
Additionally, a whole work reference does not provide any
scaffolding to help an individual understand or remember the text.
2.2.1- Citations of literary works with multiple editions, in academic writing (MLA) - Citations of theatric works (scenes) - Citations of legal code
2.2.2Specificity: 4-8
Section headers can be quite specific, but it really depends on the size of
the sections.
2.2.3Conciseness: 4-9
There is a wide range on the conciseness of section header references,
generally inversely related to the meaningfulness and specificity of the
reference: if the headers are short and specific, e.g. “Chapter 1”, then
they are relatively meaningless and probably not very specific, but if
they are meaningful and specific, then the citations will clutter the
citing work to the point of unreadability.
2.2.4Portability: 9
Section headers generally apply quite well across all versions or editions
of a text, since changes over time and differences between formats are rare.
2.2.5Meaningfulness: 6-10
Section headers can be an extremely meaningful referencing scheme, in that
each reference contains within itself a complete meaning. This is why some
reading coaches have recommended that when reading a book for information,
one should read the table of contents first; understanding the whole provides
a framework in which an understanding of each of the parts can develop.
However, as mentioned above, the meaningfulness of a section reference is
usually the inverse of its conciseness.
2.3.1- Citations of direct quotes in academic writing (APA, MLA) - Citations of paraphrased ideas in academic writing (MLA)
2.3.2Specificity: 8
Page numbers can get you within a few lines of a small selection from a
large text.
2.3.3Conciseness: 8
Page references are concise.
2.3.4Portability: 0
Page numbers simply aren’t portable. Online sources are rarely paginated
and new editions of print texts change page numbers.
2.3.5Meaningfulness: 3
Page numbers might mean something to people with very visual memory, and
they do give some indication whether a work might be very long, but since
the format of a page is not in any way related to the meaning of its content,
the page number itself is inherently meaningless as a reference.
2.4.1- Citations of computer programs - Citations of financial instruments, e.g. receipts, budgets
2.4.2Specificity: 9
The only way to get more specific is to number the characters in the line
—which is also used in referencing computer programs, e.g. when debugging.
2.4.3Conciseness: 8
Line number references are concise.
2.4.4Portability: 2 (8 for computer programs)
Line numbers are portable for computer programs, because computer programs
are stored in files of arbitrary width and length and are organized into
small statements that are most conveniently delineated by line breaks.
For most other written works, line numbers would not be portable at all,
because content of the lines would be determined by the width of the media
or rendering device.
2.4.5Meaningfulness: 2 (8 for computer programs)
Line numbers are meaningful for computer programs because they generally
contain discreet statements. For other texts, While line numbers may convey
some understanding of the probable length of a work and the position of an
idea inside of it, that really doesn’t add much to existing understanding.
2.5.1- Citations of religious works, e.g. John 3:16 - Citations of legal code, e.g. 501.c.3 - Citations of theatric works (not numbered, but as in “To be or not to be...”)
2.5.2Specificity: 8
Paragraph references will get you within a few sentences of any passage.
2.5.3Conciseness: 8
Paragraph references are concise.
2.5.4Portability: 9
Paragraph numbers can change between editions of written works or versions
of online documents, but this is quite rare. Format and medium does not
affect paragraph numbering.
2.5.5Meaningfulness: 4-10
The meaningfulness of paragraph numbers begins at a relatively low level,
but it grows in proportion to a reader’s familiarity with the content.
For content that is expected to be mastered and memorized, paragraph
references can be the glue that powerfully binds together an understanding
of each part into a comprehension of the whole. Paragraph numbered works
are surely the most widely memorized, cited, and recognized works ever;
considering only the examples above, you will probably know the general
meaning and at least a few words of each one, even if you are neither a
Christian, nor a lawyer or accountant, nor a thespian.
2.6.1Put simply, among the various reference schemes in common use, paragraph references have often provided the most beneficial balance of specificity, conciseness, portability, and meaningfulness. Whether by cause or by effect, paragraphs have been chosen as the frame of reference for all of the most easily recognizable content in history. Given all the benefits of paragraph references, we should support their use, and we can do so simply by numbering the paragraphs in our documents.
3.0.1HTML has revolutionzied the creation of written works in many ways, some of which raise difficult questions in relation to paragraph numbering.
3.1.1Since block quotes and list items can contain multiple paragraphs, is there a consistent way to number those paragraph elements? For example, if this list item contains multiple distinct ideas, and if each idea is contained in its own paragraph, then paragraph numbering needs to account for this in some way.
3.1.2There are several possible options, but for most cases we recommend (and this library explicitly supports) numbering <blockquote> and <li> tags only if they contain actual <p> tags within.
3.1.3In the case of a “tight list”, where the list items are rendered without paragraphs within, it may sometimes be appropriate to number the first list item or the list itself, especially if the list functions as a paragraph in its own right.
3.2.1Paragraph elements in HTML cannot contain lists or blockquotes. However, paragraphs in literary contexts can and often do. How does one go about numbering the element that describes a paragraph which, in its original form, contained a block quote or list?
3.2.2Consider that—as the American Psychological Association indicates— in cases where a phrase leads directly into a long quotation
you might choose to begin the block quote with a lowercase letter. In this and the later examples we use “Lorem ipsum” text to ensure that each block quotation contains 40 words or more. Lorem ipsum dolor sit amet, consectetur adipiscing elit. (APA Blog)
3.2.3In HTML, one would be obliged to end this <p> before the quote, because <blockquote>s are not permitted within <p> elements. This does not make sense, because it’s a paragraph and it needs to contain a complete idea and then end with a period! —or perhaps an exclamation mark.
3.2.4In any case, the quotation above is clearly a part of the paragraph, and it probably should not receive its own paragraph number.
3.3.1There are a few cases in which paragraphs should not be numbered.[4] Along with the above-mentioned case of a block quote that should be part of a paragraph, there are also the following examples:
3.3.21. Quotations of poetry within other texts often do not receive separate paragraph numbers.
3.3.32. The opening sentence of sections of religious texts, such as surahs in the Qur’an and many prayers, have an exhortation line that is not given its own verse or paragraph number.
3.3.43. Certain paragraphs of letters do not receive their own number, including the greeting or salutation, the complimentary close, and the signature line. For example:
Dear reader,
3.3.5I am very hopeful that you are finding this manifesto at once interesting, entertaining, and convincing, and that in the future you will always make the utmost endeavor to number your paragraphs.
3.3.6Also, I’m reconsidering the part about never using paragraphs inside list items.
Sincerely,
[Author McAuthorsson]
This text uses markdown footnotes and markdown attributes. These are extensions to Markdown and the Commonmark spec, and they are not supported in all contexts (e.g. on Github). If you see constructions like “[^1]” or {.class}, this is why. ↩
Sentences can, of course, convey a complete idea, in which case one sentence may comprise an entire paragraph, but such completeness is not expected of sentences. ↩
In the grand and time-honored tradition of all manifestos, the sections below make expansive and prescriptive declarations based solely on the opinions and inductive reasoning of the authors, no doubt giving free rein to cultural and personal bias, without reference to any studies conducted in accordance with the rigorous standards of proper science.
Therefore, a reader may simply agree—on your own peril —while a scientifically-minded reader may consider this a recommendation for future study and consideration.
Please note that disagreement is not an option. ↩
This library allows authors to skip paragraph numbering for a single paragraph using the html attributes ¶=“none” or ¶=“skip”. For multiple paragraphs, authors may use the same attribute for control, viz. ¶=“stop” or ¶=“off” to stop, and ¶=“start”, ¶=“on”, or ¶=“auto” to continue.
Attributes are not part of Markdown, but can be added with an extension such as markdown-it-attrs. ↩


Toy Wars pits two hot-seated players against each other in a turn-based PvP combat environment. Both players have their own set of toy soldiers and must use them to destroy either their opponent's soldiers or their teddy bear.
Toy Wars was a 3-month long project developed using Unity and C# as a part of my studies at the Academy of Interactive Entertainment (AIE). The project was developed by 5 games development students and was the final assessment task of the first year of studies. My role, along with one other student, was programming.
My role in programming consisted of several main tasks including the turn manager, teddy bear bases, gameplay UI and menus, as well as other various scripts required throughout development. The other programmer mainly focused on soldiers, working on the shooting mechanics and movement.
The overall goal of the Toy Wars project, other than an assignment requirement, was to create a simple, turn-based, player versus player game inspired by other games like worms and army men.
The game was intended to be a two-player hot-seat with army soldiers and teddy bears acting as units for each player. The end goal was to kill all of the enemy units or destroy the enemy Teddy Bear before the other player did the same. The project had a few hiccups along the way but mostly turned out how we intended minus a few different weapons and unit types.
During the development of the Toy Wars project, I learnt many things about teamwork and scope. Toy Wars was one of the first group video game projects most of the students had ever worked on, and as a result, everyone was very excited. We were all overconfident, thinking we could do anything, quickly realizing and falling felt to the pressure of how much work we had given ourselves. As a result, we had to scope down a lot of great original ideas we had envisioned as staples of the projects. It was a hard reality.
As a programmer on the project, there were many things I learnt. Things I thought were simple programming tasks didn't turn out to be so simple. An example of this was the turn management system, in which I implemented quickly at the start of development, moving on to other tasks thinking it was more or less complete. Quickly issues started to arise and I had to rethink my strategies, the turn management system was not simple, it was kind of complicated.
Other things I learnt during my time working on this project was the importance of planning and research, especially when it came to things like a turn management system, for example. My take away from this is I shouldn't assume something might be simple without confirming it first, as well as a better understanding of how to scope a project.


Appearance
Header color
| opacity
Window background color
| opacity
Tabs instead of windows
Advanced (beginners, go away!)
Logic tick (ms)
Statistic window
Show runtime
Refresh page function settings
Enable refresh function
Include npc black list into refresh page function
Refresh interval (min)
Clear settings storage
Speed format
uri/min.
uri/hour


Constructs a new file object
sfFilebasePluginFileObject
__construct
(
sfFilebasePluginFile $file_name,
sfFilebasePlugin $filebase, [
$open_mode $open_mode =
'r'], [
$use_include_path $use_include_path =
false], [
$context $context =
null])
-
sfFilebasePluginFile
$file_name: The name of the stream to open
-
sfFilebasePlugin
$filebase
-
$open_mode
$open_mode: The file open mode
-
$use_include_path
$use_include_path: Whether to search in include paths
-
$context
$context: A stream context
Redefinition of:
- SplFileObject::constructor __construct ( $file_name, [$open_mode = ], [$use_include_path = ], [$context = ] )
Wraps native function file_get_contents()
string
fileGetContents
([flags $flags = null], [context $context = null], [offset $offset = null], [maxlen $maxlen = null])
-
flags
$flags: int[optional]

For all versions prior to PHP 6, this parameter is called use_include_path and is a bool. The flags parameter is only available since PHP 6. If you use an older version and want to search for filename in the include path, this parameter must be true. Since PHP 6, you have to use the FILE_USE_INCLUDE_PATH flag instead.

The value of flags can be any combination of the following flags (with some restrictions), joined with the binary OR (|) operator.

 Available flags
 Flag	 Description

  FILE_USE_INCLUDE_PATH 	  Search for filename in the include directory. See include_path for more information.

  FILE_TEXT 	  If unicode semantics are enabled, the default encoding of the read data is UTF-8. You can specify a different encoding by creating a custom context or by changing the default using stream_default_encoding. This flag cannot be used with FILE_BINARY.

  FILE_BINARY 	  With this flag, the file is read in binary mode. This is the default setting and cannot be used with FILE_TEXT.

-
context
$context: resource[optional]

A valid context resource created with stream_context_create. If you don't need to use a custom context, you can skip this parameter by &null;.

-
offset
$offset: int[optional]

The offset where the reading starts.

-
maxlen
$maxlen: int[optional]

Maximum length of data read.

Returns the underlying sfFilebasePluginInfo instance.
Writes $length bytes of $str.
boolean
write
(string $str, [integer $length = null])
-
string
$str
-
integer
$length
Writes a portion of string ending by a new line delimiter with maxlength = $length bytes.
boolean
writeLn
(string $str, [ $nl_delim = "\n"], [ $length = null])
-
string
$str
-
$nl_delim
-
$length
Delegates method calls to the underlying sfFilebasePluginFile-Instance.
void
__call
( $method_name, array $args, string $method)
-
string
$method
-
array
$args
-
$method_name
Inherited Methods
Inherited From SplFileObject (Internal Class)
constructor __construct ( $file_name, [$open_mode = ], [$use_include_path = ], [$context = ] )
current ( )
eof ( )
fflush ( )
fgetc ( )
fgetcsv ( [$delimiter = ], [$enclosure = ] )
fgets ( )
fgetss ( [$allowable_tags = ] )
flock ( $operation, [&$wouldblock = ] )
fpassthru ( )
fscanf ( $format )
fseek ( $pos, [$whence = ] )
fstat ( )
ftell ( )
ftruncate ( $size )
fwrite ( $str, [$length = ] )
getATime ( )
getBasename ( [$suffix = ] )
getChildren ( )
getCsvControl ( )
getCTime ( )
getCurrentLine ( )
getFileInfo ( [$class_name = ] )
getFilename ( )
getFlags ( )
getGroup ( )
getInode ( )
getLinkTarget ( )
getMaxLineLen ( )
getMTime ( )
getOwner ( )
getPath ( )
getPathInfo ( [$class_name = ] )
getPathname ( )
getPerms ( )
getRealPath ( )
getSize ( )
getType ( )
hasChildren ( )
isDir ( )
isExecutable ( )
isFile ( )
isLink ( )
isReadable ( )
isWritable ( )
key ( )
next ( )
openFile ( [$open_mode = ], [$use_include_path = ], [$context = ] )
rewind ( )
seek ( $line_pos )
setCsvControl ( [$delimiter = ], [$enclosure = ] )
setFileClass ( [$class_name = ] )
setFlags ( $flags )
setInfoClass ( [$class_name = ] )
setMaxLineLen ( $max_len )
valid ( )
__toString ( )


class Bundler::Dir
Public Class Methods
Bundler::Dir.mktmpdir
creates a temporary directory.
The directory is created with 0700 permission. Application should not change the permission to make the temporary directory accessible from other users.
The prefix and suffix of the name of the directory is specified by the optional first argument, prefix_suffix.
-
If it is not specified or nil, “d” is used as the prefix and no suffix is used.
-
If it is a string, it is used as the prefix and no suffix is used.
-
If it is an array, first element is used as the prefix and second element is used as a suffix.
Bundler::Dir.mktmpdir {|dir| dir is ".../d..." } Bundler::Dir.mktmpdir("foo") {|dir| dir is ".../foo..." } Bundler::Dir.mktmpdir(["foo", "bar"]) {|dir| dir is ".../foo...bar" }
The directory is created under Bundler::Dir.tmpdir
or the optional second argument tmpdir if non-nil value is given.
Bundler::Dir.mktmpdir {|dir| dir is "#{Bundler::Dir.tmpdir}/d..." } Bundler::Dir.mktmpdir(nil, "/var/tmp") {|dir| dir is "/var/tmp/d..." }
If a block is given, it is yielded with the path of the directory. The directory and its contents are removed using Bundler::FileUtils.remove_entry
before Bundler::Dir.mktmpdir
returns. The value of the block is returned.
Bundler::Dir.mktmpdir {|dir| # use the directory... open("#{dir}/foo", "w") { ... } }
If a block is not given, The path of the directory is returned. In this case, Bundler::Dir.mktmpdir
doesn't remove the directory.
dir = Bundler::Dir.mktmpdir begin # use the directory... open("#{dir}/foo", "w") { ... } ensure # remove the directory. Bundler::FileUtils.remove_entry dir end
# File lib/bundler/vendor/tmpdir/lib/tmpdir.rb, line 88 def self.mktmpdir(prefix_suffix=nil, *rest, **options) base = nil path = Tmpname.create(prefix_suffix || "d", *rest, **options) {|p, _, _, d| base = d mkdir(p, 0700) } if block_given? begin yield path.dup ensure unless base stat = File.stat(File.dirname(path)) if stat.world_writable? and !stat.sticky? raise ArgumentError, "parent directory is world writable but not sticky" end end Bundler::FileUtils.remove_entry path end else path end end
Returns the operating system's temporary file path.
# File lib/bundler/vendor/tmpdir/lib/tmpdir.rb, line 21 def self.tmpdir tmp = nil ['TMPDIR', 'TMP', 'TEMP', ['system temporary path', @systmpdir], ['/tmp']*2, ['.']*2].each do |name, dir = ENV[name]| next if !dir dir = File.expand_path(dir) stat = File.stat(dir) rescue next case when !stat.directory? warn "#{name} is not a directory: #{dir}" when !stat.writable? warn "#{name} is not writable: #{dir}" when stat.world_writable? && !stat.sticky? warn "#{name} is world-writable: #{dir}" else tmp = dir break end end raise ArgumentError, "could not find a temporary directory" unless tmp tmp end


This site accepts speedruns for all Capcom-official Master Rank Hunt or Slay quests where the target is one or more large monsters. Runs are divided into two main rulesets - TA Wiki Rules and Freestyle. Individual run categories are divided by quest, weapon, platform, and ruleset. Only WR runs in a category will be accepted, and Freestyle runs must be faster than the fastest TA run.
Freestyle Rules
Runs must be shown in their entirety, from the "Begin Quest" screen to the "Main objective complete!" screen.
Show damage numbers.
Show gear, skills, and food skills, preferably during the quest.
TA Rules
TA runs must follow any gameplay rules listed here. The site deviates from the TA Wiki on the following rules:
PC runs are allowed.
Runs on YouTube and bilibili are allowed.
Reporting game version number is not required.
Editing out the wait at the end of quests is allowed. Runs must be shown from the "Begin Quest" screen to the "Main objective complete!" screen.
Cheating
Any runner caught cheating will have all of his or her runs removed from the site, and will no longer be welcome to submit new runs. Don't cheat - there are no prizes here.
The following is allowed:
Altering the HP or size of a monster to values possible in normal gameplay
Custom quests replicating official Capcom quests, so long as the replica produces a game state possible through normal play in an offical Capcom quest
Tools guaranteeing food skills, so long as the combination of food skills is possible in normal gameplay
Any cosmetic changes, so long as monsters and damage numbers remain identifiable.
The following counts as cheating:
Altering the HP or size of a monster to values not possible in normal gameplay
Altering player or Palico damage in any way
Altering the effects of armor or food skills in any way
Altering player or Palico equipment in any non-cosmetic way
Altering Spare Shot or True Spare Shot proc rates, or altering Affinity
Altering monster behavior in any way
The following is strongly discouraged, but due to undetectability, is allowed:
Use of overlay
Altering Palico behavior, so long as the behavior is possible unmodded
Use of the "Better Input Detection" mod by MoonBunnie and AsteriskAmpersand
If a modification is not mentioned above, it will by default count as cheating. If you would like to clarify whether or not something is legal, feel free to DM me on Discord at String#8918.


This package contains code for Clustering, the base class
of a Cluster is org.apache.catalina.Cluster
implementations
of this class is done when implementing a new Cluster protocol
The only Cluster protocol currently implemented is a MulticastProtocol
StandardCluster.java
In addition to the Cluster
implementation
StandardCluster
there's a listener and a sender implemented
using multicasting.
ClusterSender
implementation that works
with a MulticastSocketClusterReceiver
implementation that works
with a MulticastSocketBoth extend the common class ClusterSessionBase
which provides common
functionality shared by the two implementations.


Outdoor Cinema
Harewood House, Leeds, West Yorkshire, LS17 9LG
Leeds Gay Pride
5 August 2018
Leeds Festival
23-25 August 2019


--- layout: post status: publish published: true title: VS2008 Beta 2 VMs Expiration author: display_name: Tomas Restrepo login: tomasr email<EMAIL_ADDRESS>url: http://winterdom.com/ author_login: tomasr author_email<EMAIL_ADDRESS>author_url: http://winterdom.com/ wordpress_id: 916 wordpress_url: http://winterdom.com/2007/10/vs2008beta2vmsexpiration date: '2007-10-30 05:28:11 +0000' date_gmt: '2007-10-30 05:28:11 +0000' categories: - ".NET" tags: [] comments: - id: 637 author: Ricky Dhatt author_email<EMAIL_ADDRESS>author_url: '' date: '2007-10-30 13:37:40 +0000' date_gmt: '2007-10-30 13:37:40 +0000' content: | From current download page: "The Beta 2 VPC images downloaded before October 29 will expire on November 1, 2007, which is earlier than anticipated. After that time, the VPC will operate for only 1-2 hours before needing to be restarted. This restart can be eliminated by upgrading the underlying installation of Windows Server on the original VPC image. Also, new VPC images have been posted below. " ---
As most will probably know, turns out the Visual Studio 2008 Virtual Machines had an OS expiration date of Nov 1st, so they'll soon stop working. According to Jeff Beehler, the updated VMs have been posted for download now.
However, when I visit the download page linked in his post, the first thing I notice is this big bold announcement:
THESE IMAGES WILL EXPIRE ON NOVEMBER 1, 2007. UPDATED IMAGES WILL BE AVAILABLE AFTER OCTOBER 29, 2007.
So are these the updated files or not? Does anybody know?
Update: The page has been updated and it seems the new downloads are really up this time. Fingers crossed ;-)


xtrctl
Synopsis
xtrctl [–help] <command> [<args>] <socket path>
Description
xtrctl is a command line tool that can be used to query the status of log sinks, modify log levels and reopen log files (for rotation) for the xtr logger.
Commands
Querying Sink Status
xtrctl status [options] [pattern] <socket path>
The status command displays status information for sinks matching the given pattern, or for all sinks if no pattern is specified. The sink name, current log level, buffer capacity, current used buffer space and number of dropped log messages are displayed. For example:
ExampleName (info) 64K capacity, 0K used, 0 dropped
For an explanation of pattern and options please refer to the see PATTERNS and see OPTIONS sections.
Setting Log Levels
xtrctl level <level> [options] [pattern] <socket path>
The level command sets the log level to level for sinks matching the given pattern, or for all sinks if no pattern is specified. Valid values for level are ‘none’, ‘fatal’, ‘error’, ‘warning’, ‘info’ or ‘debug’ (please refer to the log levels section of the API reference or libxtr(3)).
Reopening Log Files
xtrctl reopen <socket path>
The reopen command simply reopens the log file (if a file is being logged to). This command exists to support log file rotation by integrating with existing tools such as logrotate(8), for example by sending a reopen command from the postrotate script defined in logrotate’s configuration file:
/path/to/your/log {
rotate 5
weekly
postrotate
xtrctl reopen /path/to/xtrctl/socket
endscript
}
Patterns
In the status and level commands pattern is a regular expression or wildcard that may be used to selectively apply the command to sinks with names matching the given pattern. If no pattern is specified then the command applies to all sinks. By default the pattern is interpreted as a basic regular expression, to use an extended regular expression or wildcard please refer to the OPTIONS section.
Options
The status and level commands support the following options:
- -E, –extended-regexp
Interpret pattern as extended regular expressions (see regex(7)).
- -G, –basic-regex
Interpret pattern options as basic regular expressions (see regex(7)).
- -W, –wildcard
Interpret pattern options as shell wildcard patterns (see glob(7)).
Socket Paths
Please refer to the documentation for xtr::default_command_path()
in
the API reference or libxtr(3) for an explanation of how the default
socket path is constructed. Custom command paths may be specified either by
passing an argument to xtr::logger::logger()
or by calling
xtr::logger::set_command_path()
, the documentation for both may also
be found in the API reference or libxtr(3).
Examples
Querying the status of all sinks:
> xtrctl status /run/user/1000/xtrctl.7852.0
Test0 (info) 64K capacity, 0K used, 0 dropped
Test1 (info) 64K capacity, 0K used, 0 dropped
Test2 (info) 64K capacity, 0K used, 0 dropped
Test3 (info) 64K capacity, 0K used, 0 dropped
Test4 (info) 64K capacity, 0K used, 0 dropped
Setting the level of all sinks to ‘error’:
> xtrctl level error /run/user/1000/xtrctl.7852.0
Success
Setting the level of sinks matching a pattern to ‘warning’:
> xtrctl level warning 'Test[0-2]' /run/user/1000/xtrctl.7852.0
Success
Querying the status of sinks matching a pattern:
> xtrctl status 'Test[0-2]' /run/user/1000/xtrctl.7852.0
Test0 (warning) 64K capacity, 0K used, 0 dropped
Test1 (warning) 64K capacity, 0K used, 0 dropped
Test2 (warning) 64K capacity, 0K used, 0 dropped
Accessing xtrctl via Conan
Add a virtualenv generator, for example if using conanfile.txt:
[generators]
virtualenv
...etc..
After running conan install
run source activate.sh
and xtrctl
should be in your $PATH.


{% extends "global/Page.html" %} {% load staticfiles otree_tags %} {% block title %} Description {% endblock %} {% block content %}
You will be paired with a {{ player.other_role }} and you will play 1 round of this version of the Task Game. After this, you will be randomly paired with a different {{ player.other_role }} and you will play {{ constants.number_rounds_per_partner }} more rounds with this partner, and so on until you have played with five different partners (40 rounds in all). At the end of the experiment, five rounds will be randomly selected and you will be paid out from your total earnings from those rounds.
{% next_button %} {% endblock %}


Ultimate get user project- ΑΦΜ
Give me something to search around for information!
Reset
Gimme Value
Press Here (Search Link)


An implementation of evaluation metrics in R that are commonly used in supervised machine learning. It implements metrics for regression, time series, binary classification, classification, and information retrieval problems. It has zero dependencies and a consistent, simple interface for all functions.
| Version: | 0.1.4 |
| Suggests: | testthat |
| Published: | 2018-07-09 |
| Author: | Ben Hamner [aut, cph], Michael Frasco [aut, cre], Erin LeDell [ctb] |
| Maintainer: | Michael Frasco <mfrasco6 at gmail.com> |
| BugReports: | https://github.com/mfrasco/Metrics/issues |
| License: | BSD_3_clause + file LICENSE |
| URL: | https://github.com/mfrasco/Metrics |
| NeedsCompilation: | no |
| CRAN checks: | Metrics results |
| Reference manual: | Metrics.pdf |
| Package source: | Metrics_0.1.4.tar.gz |
| Windows binaries: | r-devel: Metrics_0.1.4.zip, r-release: Metrics_0.1.4.zip, r-oldrel: Metrics_0.1.4.zip |
| OS X binaries: | r-release: Metrics_0.1.4.tgz, r-oldrel: Metrics_0.1.4.tgz |
| Old sources: | Metrics archive |
| Reverse imports: | dblr, iml, MetaIntegrator, predtoolsTS, specmine |
| Reverse suggests: | featurefinder |
Please use the canonical form https://CRAN.R-project.org/package=Metrics to link to this page.


To use Flux, you must install it to your device using Cydia Impactor
Impactor is a tool by Saurik (the guy who made Cydia) that is used to install apps from outside the App Store. It is fully safe.
To install Flux, you first download the .ipa file using the link below. Next, you open Impactor on your computer, and plug in your device. Drag and drop the file you downloaded over your device's name. You will be asked for your Apple ID email and password. Put them in. This is safe (as far as anyone knows) and only sent to Apple. Once this is done, the app will install.
NOTE: FluxInj3ct should work properly, but in case it does not, we are not responsible for any loss of property or time.
MD5: 111b6e0cf92842fc7b9d47378c4bde86
SHA256: 11bc571580a38a6ccf4548a90ad59483f7972006c89465ecf14e2bca65d49445
SHA1: ae2169821ba4e72876ea6f86efa174f93ec571a0


4 Describe data in a metadata file
Your raw flat data files should only include data, no comments. Rather than using complicated spreadsheets, create a metadata document, often called “README”, that includes (at a minimum) what data you are collecting, how and when the data were collected, where the data is stored, and who owns the data. This file should also include a “data dictionary” that describes each variable and associated unit in the data file (see example below). Be as specific as possible; for example, the description for fish length variables should include whether it is fork length, total length, or standard length.
4.1 Use clear and concise descriptive names for data files and variable names
File names are the easiest way to explain the contents of a data file. Capturing the place, time, and content of the data, even in an abbreviated fashion, can be extremely useful. For example, consider naming a fish catch monitoring file “muni_fishcatch_month_year.csv”, replacing “muni”, “month”, and “year” with the appropriate values. Similarly, each column in your data should contain a unique variable and be given a clear but concise name that uses letters, numbers, dashes, dots, or underscores. Lastly, always use plain ASCII text, as certain marks (e.g., accents) or characters (e.g., Chinese or Japanese) are not widely supported.
File and variable names should NOT be overly long or contain spaces or special characters (e.g. &$%@) + YES: filename: muni_fishcatch_month_year.csv* | variables: year; first_name; last.name; TotalLength + NO: filename: November 2007.csv | variables: Start Year; First Name; Total weight (kg); $ Value
4.2 Always use consistent formats for data values and (if necessary) put units in a separate column
When entering data, do not mix text and numeric responses, or include both text and numbers in the same response. Periods are okay to include for numeric responses but avoid commas (commas indicate a new value in a .csv file). For text values, such as a person’s name or location, take care not to change capitalization, spelling, spacing, etc. (e.g. John, john, jon) as this will generate confusion. Consider using identification codes for variables with many possible categories (e.g., local species name, gear type). Units should always be in their own column or absent entirely but explained in the metadata file. Also, do not use color coding, it cannot be interpreted by other software (data in red below are just to demonstrate improper data entries).
When reporting full dates, use standardized formats since date representations vary between the United States and the rest of the world. For example, 01-09-17 will likely be interpreted as January 9th, 2017 in the U.S. but September 1st, 2017 or September 17th, 2001 in other countries. Therefore, always record dates using the international standard of YYYY-MM-DD as prescribed by the International Organization of Standards (ISO) standard ISO 8601 (2004). It is also generally good to have separate columns for month and year.
4.3 Always store an uncorrected original version of the data file and BACK UP YOUR DATA!!!
When you make changes or corrections to the original data file you could easily make a mistake. To avoid compromising your original raw data, always store an unadjusted copy of the data file and do not make any changes or adjustments to this copy (make it “read-only” if possible). Make a duplicate file if corrections or adjustments are required and be sure to document what changes were made in the metadata file. Lastly, ALWAYS BACK UP YOUR DATA by keeping at least three copies of the file in different locations (e.g., desktop, external hard drive, the cloud).


Attacker
zombies, popo, Hong Kong police,
riot police, elite police,
special agents
30,000+
Defender
citizen
2 Million / 7 Million
Weapons
Attackers:
Baton, Shield, Pepper Spray, Tear Gas,
Rubber bullet, Bean bag bullet,
Pistol, Assult rifle, Sniper rifle,
Armed vehicle
Defenders:
Mobile phone, Internet connection
Commanders & Leaders
Attacker:
HK Gov.,
Police Centers,
Mainland superpower
Defenders:
Leaderless


‘Lesotho’s win our best result on synthetic pitch’ - The Nation Newspaper
Super Eagles Coach, Gernot Rohr has described the 4-2 win over Lesotho in the 2021 AFCON Qualifiers as the best result by the Super Eagles on a synthetic pitch.
He, however, lauded Victor Osimhen, for his contribution in the win. “It was one of the best results on a synthetic pitch which is not easy to play. Again they got a goal in the beginning and we had to react.
“But we now know that the confidence they (Eagles) have because they play good football and when they play good football, you can have the goals and the goals are coming very nice; constructive goals and Osimhen is on the right way to be a good scorer and also not to forget players like Odion Ighalo.
“I think Osimhen is on the way to becoming a very important player for us as he scored more goals and he gave also his second goal to his friend, Chukwueze. So he (Osimhen) is working very well in France and I see him often playing and speaking with his coach. We know he still has lots of work to do to progress because he is young and he can do more,” Rohr said.
He added: “He (Osimhen) changed the second half. It is interesting to see a 3-5-2 with Osimhen and Chukwueze as the twin strikers. So, the second half was better and this match is very important for the future.”


{% extends "layout.html" %} {% block body %}
That email address is already in use. Please login or contact support if you have forgotten your login details.
There was an error. You have not been registered.Please contact support.
Have an account? Sign in here


ICBC Payment Plan - Pre-Authorized Debit
Help
Cancel
Institution Number
001 – Bank of Montreal (BMO)
002 – The Bank of Nova Scotia (Scotiabank)
003 – Royal Bank of Canada (RBC)
004 – Toronto-Dominion Canada Trust (TD)
006 – National Bank of Canada (NBC)
010 – National Bank of Canada (NBC)
016 – HSBC
Transit Number
Account Number


This is the Record specification for calendar items used by the CalendarPanel's calendar store. If your model fields are named differently you should update the mapping configs accordingly. The only required fields when creating a new calendar record instance are CalendarId and Title. All other fields are either optional or will be defaulted if blank.
More...
This is the Record specification for calendar items used by the CalendarPanel's calendar store. If your model fields are named differently you should update the mapping configs accordingly. The only required fields when creating a new calendar record instance are CalendarId and Title. All other fields are either optional or will be defaulted if blank.
◆ CalendarId
| virtual ? int Ext.Net.CalendarModel.CalendarId |
|
getset |
◆ Description
| virtual string Ext.Net.CalendarModel.Description |
|
getset |
◆ IsHidden
| virtual ? bool Ext.Net.CalendarModel.IsHidden |
|
getset |
◆ Title
| virtual string Ext.Net.CalendarModel.Title |
|
getset |
The documentation for this class was generated from the following file: