After downloading the workshop Jupyter notebook, you can upload it to Google Colab to get a quick start, but you will not be able to see the animations.
Overview
Machine Learning and Optimization Seminar
The goal of the Machine Learning and Optimization seminar this year is to expose the participants to topics in machine learning and optimization by:
Facilitating hands-on workshops and group discussions to explore and gain experience
Inviting speakers to introduce machine learning and optimization concepts
We are excited to get started on this but recognize that since neither machine learning nor optimization are standardized in the department, participants will have a varied level of exposure to different topics. Our hope is that we can use this disparity of experience to increase collaboration during the workshops in a way that can’t be achieved during the talks. All are encouraged to share their knowledge and experience with one another openly during the workshops and to give feedback to the organizers after.
All workshop material will be available here for later reference.
This first workshop is focused on tooling and the basic concepts of machine learning and optimization with the goal that everyone can be on the same footing for later workshops.
Your Python environment
It is safe to say that Python is the language of choice for machine learning. This interpreted language has a very clear and high-level syntax, is extremely convenient for interactive programming and debugging, and has an enormous user base of enterprises, researchers, and hobbyists who have built an almost infinite collection of open-source packages for every topic. For these three reasons the workshops for this seminar will use Python.
To get started, we will give the basics of the Python programming language. Just kidding! That would take too long. We will instead guide you on how to install Python most conveniently, teach you how to get started learning about Python, and then point you to a curated library of much more high-quality instruction for using Python. A list of some such references as well as documentation for setup and important Python packages can be found in the Appendix here.
Setting up
Tip
If you are looking for the easiest and most immediate way to get going with a Python Jupyter Notebook, check out the Google Colab section.
Note
This section will require use of a terminal emulator for installing and running Python. If you are not familiar with the terminal, check out this quick tutorial to get started.
If you are using a computer with Windows, the terminal instructions may not apply.
Python is installed by default on MacOS and most Linux distributions. However, it can be challenging to navigate between the versions and packages that your operating system uses and those needed for other projects. Thus, there are a variety of version, package, and environment management tools:
Version management: Which version of Python are you using? Can you change versions to run a specific Python program if it requires?
pyenv
conda/mamba
Package management: How can you install the many amazing Python packages people have created?
pip
conda/mamba
Environment management: If you have two projects that require different packages (or different versions of the same package), can you switch which packages are available depending on which project you are working on?
venv
virtualenv
poetry
conda/mamba
many more
The conda package manager is the only one that fills all three roles. It is formally a part of the Anaconda Python distribution which is a favorite in the fields of data science and machine learning. mamba is a newer and faster rewrite used in exactly the same way and which is highly recommended.
The best way to get started with mamba is to install mambaforge. You can find installer downloads for Windows, MacOS, or Linux here.
For Windows, run the .exe file once it is downloaded.
For MacOS and Linux, open a terminal and navigate to the download location:
cd ~/Downloads
Then run the installer as follows:
./Mambaforge-Linux-x86_64.sh
The installer will walk you through a few steps and end by asking if you’d like to “initialize Mambaforge by running conda init?” Answer yes and restart your terminal. This final command will have added conda and mamba to your system $PATH variable, which means it is available to your terminal. Once restarted, run mamba -V to print the version and to verify that the installation worked.
Environments
The idea of a conda/mamba environment is that once an environment is created and activated, all new packages installed will be added to that environment and will be accessible to any Python program run while the environment is active. As an example, let’s create an environment called workshop with a specific version of Python installed. The following will create the environment and install a specific version of python:
mamba create -n workshop python=3.9
Once created, we can list our environments via the command
mamba env list
# conda environments:
#
base /home/user/mambaforge
workshop /home/user/mambaforge/envs/workshop
Note that there is a “base” environment which is where conda and mamba themselves are installed as well as their dependencies. The best practice is to create an environment for each of your projects to minimize dependency issues (when packages require separate versions of the same package).
To activate our new environment:
mamba activate workshop
Running mamba env list will now show our active environment via an asterisk:
base /home/user/mambaforge
workshop * /home/user/mambaforge/envs/workshop
Installing packages
Now that we have activated the workshopconda environment, let’s install some common machine learning packages in Python. It is as easy as writing:
This command will search the conda-forge repository of packages and install the most up-to-date versions (the forge in mambaforge).
Tip
Either conda or mamba could be used for all the commands discussed in this section. However, mamba is significantly faster when installing packages.
Now that these packages have been installed, we can easily use them in an interactive ipython prompt (installed with the jupyter package):
ipython
Python 3.9.13 | packaged by conda-forge | (main, May 27 2022, 16:58:50)Type 'copyright', 'credits' or 'license' for more informationIPython 8.4.0 -- An enhanced Interactive Python. Type '?' for help.In [1]: import numpy as npIn [2]: import matplotlib.pyplot as pltIn [3]: x = np.linspace(0,10,100)In [4]: y = np.sin(x)In [5]: plt.plot(x,y); plt.show()
This should plot a simple \(\sin\) curve.
Cleaning up
After we are done using the environment that has our desired version of Python and the needed packages, we can go back to our regular terminal by deactivating the environment:
mamba deactivate workshop
If we have somehow broken our environment and need to remove it:
mamba env remove -n workshop
There are many more commands and functionalities that conda and mamba provide that can be found in the python resources and packages section of the Appendix.
Google Colab
As an alternative to the entire procedure above, you can use an online Jupyter Notebook service hosted by Google called Colab. This service will get you up and running immediately but cannot save your environment between notebooks and has limited functionality to run scripts, save data, view animations, change package versions, etc. Thus, if your notebook requires a package that is not installed by default, you will need to add the installation command in one of the first notebook cells. For example, to install the reservoirpy package, we would write in a notebook cell:
!pip install reservoirpy
In a notebook, the ! denotes a terminal command. The package will now be ready for import and use within the current notebook session:
from reservoirpy.nodes import Input,Reservoir,Ridge
Basic concepts of machine learning
Machine learning is, at its most basic, automated data analysis, usually with the goal of finding patterns or making predictions. The “machines” in this analysis are equations or algorithms and the “learning” is usually some form of parameter selection and/or fitting. Due to the uncertain nature of most data, the majority of these models are probabilistic in nature. In fact, it can be hard to distinguish the methodological lines between what is termed “machine learning” and the field of statistics. However, there are a few important distinctions between the tools, goals, and terminology of the two areas. Today, machine learning has emerged as a broad description of almost any data-driven computing which may or may not include classical descriptive and inferential statistics [1].
At first glance, machine learning can be separated into three main classes:
Supervised learning: Given dependent and independent variable data, train a model which effectively maps the independent variable data to produce the dependent variable data.
Generalized linear models (linear, logistic, etc. regression)
Naive Bayes
Neural networks (most)
Support vector machine (SVM)
Random forests
etc.
Unsupervised learning: Given data, find patterns (no specified output, though there is still a measure of success)
Clustering
Mixture models
Dimensionality reduction
Association rules
etc.
Reinforcement learning: Given input data and desired outcomes, simulate and use the results to update a model to improve the simulation’s ability to achieve those outcomes
Q-learning
SARSA
etc.
There is an enormous amount of current interest in machine learning methods and there is a corresponding amount of high-quality material discussing it. We will end the introduction here and direct you to established textbooks [1–3], NJIT classes (Math 478, Math 678, Math 680, CS 675, CS 677), and online resources (too many to even start listing).
General procedure
In practice, machine learning algorithms often boil down to an optimization problem. To characterize this in a few steps, consider a problem with data \(x\):
Select a model representation \(f\) with parameters \(p\) for the problem: \[
y = f(x;p)
\]
Determine an appropriate objective function \(\mathcal{L}\): \[
\mathcal{L}(f(x;p),x)
\]
Use an optimization method \(\mathcal{O}\) with parameters \(d\) to find parameters \(p\) that minimize or maximize the objective for the model: \[
p^* = \mathcal{O}(\mathcal{L},f,x;d)
\]
In some sense, this is the same procedure used for inverse problems in traditional applied mathematics but with a broader set of models \(f\) that may or may not be based on first-principles understanding of the problem.
Incorporating data
Depending on the class of problem considered (supervised, unsupervised, or reinforcement), there are a variety of choices for models \(f\) and objectives \(\mathcal{L}\). For supervised learning (the most common), the objective is often to predict or generate the output or dependent variable data of some process. For this, data can be separated into three sets:
Training data (\(x\)): used to tune the parameters \(p\)
Validation data (\(x^v\)): used to evaluate the generalization of the model \(f\) to data not in the training set during training
Testing data (\(x^t\)): used to benchmark the predictive or generative ability of the model after training is completed
A first machine learning problem
Note
The code for this problem will require the following packages: numpy, matplotlib, autograd
These workshops are about learning by doing, so let’s build understanding by fitting a simple “machine” to some data as a supervised problem. Consider some data \((x,y)\):
Data generation
import numpy as npimport matplotlib.pyplot as plt# To see animations in a Jupyter notebook, uncomment the following line:# %matplotlib notebookdef f_known(x): part1 = np.exp(-x)*(np.sin((x)**3) + np.sin((x)**2) - x) part2 =1/(1+ np.exp(-1*(x-1)))return part1 + part2xsamples = np.random.uniform(-1/2,5,100)ysamples = f_known(xsamples)plt.scatter(xsamples,ysamples)plt.xlabel("$x$"); plt.ylabel("$y$"); plt.title("Data")plt.show()
We would like to fit a model of the following form to this data: \[
f(x;p_0,p_1) = e^{-p_0x}(\sin((p_0x)^3) + \sin((p_0x)^2) - p_0x) + \frac{1}{1 + e^{-p_1(x-1)}}
\]
To formulate this as a machine learning/optimization problem, we can consider an objective/loss to minimize the \(L^2\) norm distance between the model output \(f(x)\) and the true data \(y\): \[
\mathcal{L(f(x;\vec{p}),y)} = ||f(x) - y||_2^2
\] The problem can then be written as the unconstrained optimization problem: \[
p^* = \underset{\vec{p}}{\text{minimize }} \mathcal{L}(f(x;\vec{p}),y)
\] We then expect our model \(f(x;p^*)\) to represent a “machine” that has accurately “learned” the relationship between \(x\) and \(y\).
There are several ways to approach this problem, but a simple and popular approach for a continuous and unconstrained problem is to use an iterative gradient method.
Gradient descent
Gradient descent is a straightforward method taught early in an undergraduate numerical methods class. Its simplicity and relatively low computational cost has made it popular for machine learning methods (which can contain enough parameters that second-order methods, like Newton’s method, are infeasibly expensive because of the Hessian computation). Beginning with an initial parameter guess \(\vec{p}_0\), its update procedure can be written as: \[
\begin{align*}
\vec{p}^{i+1} &= \vec{p}^i + v^i \\
v^i &= -\alpha \nabla_p \mathcal{L}
\end{align*}
\] where \(\alpha\) controls the step size in the direction of the gradient (usually called a “learning rate” in machine learning). This method will follow the gradient of the objective/loss \(\mathcal{L}\) until the objective is sufficiently small, or until it reaches a steady state.
Simply implemented in Python, this method can be written as:
def gradient_descent(f_p,x0,alpha=.2,tol=1e-12,steps=1000): x = x0 xs = [x]for s inrange(steps): v_i =-alpha*f_p(x) xnew = x + v_iif np.linalg.norm(f_p(xnew)) < tol:print("Converged to objective loss gradient below {} in {} steps.".format(tol,s))return x,xselif np.linalg.norm(xnew - x) < tol:print("Converged to steady state of tolerance {} in {} steps.".format(tol,s))return x,xs x = xnew xs.append(x)print("Did not converge after {} steps (tolerance {}).".format(steps,tol))return x,xs
However, this method contains a troublesome parameter \(\alpha\) which, if chosen too large, could prevent convergence of the solution or, if chosen too small, could require an unreasonable number of steps to converge. The method itself is also prone to terminate in local minima rather than in a global minimum unless the correct initial guess and learning rate are chosen. For this reason, “vanilla” (or normal) gradient descent is almost always replaced with a modified method in learning problems [4,5].
The following demonstrates an animation of the above gradient descent method applied to our data with two different learning rates, one successful, one not. It uses the following animation code and the autograd automatic differentiation library that will be further discussed later:
Animation code
from matplotlib import animation as animfrom matplotlib import gridspecfrom mpl_toolkits.mplot3d import Axes3D# To display the animations in a jupyer notebook uncomment the following line:# %matplotlib notebookdef animate_steps_2d(xs,loss,xmin=-.1,xmax=2.5,ymin=-1,ymax=3,interval=50): fig = plt.figure(figsize=(10,6),constrained_layout=True) gs = gridspec.GridSpec(ncols=6,nrows=2,figure=fig) ax = fig.add_subplot(gs[:,0:4],projection="3d") ax1 = fig.add_subplot(gs[0,4:]) ax2 = fig.add_subplot(gs[1,4:]) ax.view_init(47,47) ax.set_xlabel("$p_0$"); ax.set_ylabel("$p_2$"); ax.set_zlabel("loss",rotation=90) ax1.set_xlabel("$p_0$"); ax1.set_ylabel("loss") ax2.set_xlabel("$p_1$"); ax2.set_ylabel("loss") xs_arr = np.array(xs) fxs = np.linspace(xmin,xmax,100) fys = np.linspace(ymin,ymax,100) loss_fx = [loss([fxs[j],xs[0][1]]) for j inrange(len(fxs))] loss_fy = [loss([xs[0][0],fys[j]]) for j inrange(len(fys))] X,Y = np.meshgrid(fxs,fys) Z = np.zeros_like(X)for i inrange(X.shape[0]):for j inrange(X.shape[1]): Z[i,j] = loss([X[i,j],Y[i,j]])# Add surface plot surf = ax.plot_surface(X,Y,Z,cmap="gist_earth") ax1.set_xlim(np.min(X),np.max(X)); ax1.set_ylim(np.min(Z),np.max(Z)) ax2.set_xlim(np.min(Y),np.max(Y)); ax2.set_ylim(np.min(Z),np.max(Z)) plot1 = ax.plot(xs[0][0],xs[0][1],loss(xs[0]),zorder=100,color="red",linestyle="",marker="o")[0] plot2 = ax.plot([],[],[],color="orange")[0]# Add flat plots for perspective plot3 = ax1.plot(fxs,loss_fx)[0] plot4 = ax1.scatter(xs[0][0],loss(xs[0]),color="red",s=100,zorder=100) plot5 = ax2.plot(fys,loss_fy)[0] plot6 = ax2.scatter(xs[0][1],loss(xs[0]),color="red",s=100,zorder=100)def anim_func(i): x_loss = loss(xs[i]) plot1.set_data_3d(xs[i][0],xs[i][1],x_loss) temp_x1 = [xs[j][0] for j inrange(i)] temp_x2 = [xs[j][1] for j inrange(i)] temp_losses = [loss(xs[j]) for j inrange(i)] plot2.set_data_3d(temp_x1,temp_x2,temp_losses) loss_fx = [loss([fxs[j],xs[i][1]]) for j inrange(len(fxs))] loss_fy = [loss([xs[i][0],fys[j]]) for j inrange(len(fys))] plot3.set_data(fxs,loss_fx) plot4.set_offsets([xs[i][0],x_loss]) plot5.set_data(fys,loss_fy) plot6.set_offsets([xs[i][1],x_loss]) plots = [plot1,plot2,plot3,plot4,plot5,plot6]return plots tanim = anim.FuncAnimation(fig,anim_func,interval=50,frames=len(xs),blit=True)return tanim
Although this behavior is somewhat typical of vanilla gradient descent, this model was pathologically chosen to be challenging. The curvature of the loss function for each parameter at the correct parameter values are as follows:
Plotting parameter loss curves
ps = np.linspace(-.1,2.5,1000)fig,axs = plt.subplots(1,2,sharey=True,figsize=(9,4))ax1,ax2 = axsax1.plot(ps,[loss(np.array([p_i,1])) for p_i in ps])ax1.set_xlabel("$p_0$"); ax1.set_ylabel("Loss")ax2.plot(ps,[loss(np.array([1,p_i])) for p_i in ps])ax2.set_xlabel("$p_1$")# plt.xlabel("$x$"); plt.ylabel("$y$"); plt.title("Model")plt.show()
In this loss, \(p_0\) demonstrates a plethora of local minima which could trap the descent algorithm while \(p_1\) has a small gradient which will slow down the convergence. The following methods are meant to simplify the choice of a learning rate while overcoming these specific convergence issues.
Adaptive steps
Due to the challenges of determining a good learning rate (especially in models with many parameters and large variance in loss gradients), many methods have been developed to automatically adjust the \(\alpha\) parameter with each step and for each parameter. One of the most common adaptive algorithms is called adagrad (for adaptive gradient. very creative). Originally developed to provide parameter specific learning rates in sparse problems (in the case that some parameters are only occasionally important) it scales the learning rate by a squared sum of previous gradients:
def adagrad(f_p,x0,alpha=.2,tol=1e-12,steps=1000): x = x0 xs = [x]# --------- NEW ----------- sum_sq_grad =0for s inrange(steps): sum_sq_grad = f_p(x)**2+ sum_sq_grad v_i =-alpha*f_p(x)/np.sqrt(sum_sq_grad)# ------------------------- xnew = x + v_iif np.linalg.norm(f_p(xnew)) < tol:print("Converged to objective loss gradient below {} in {} steps.".format(tol,s))return x,xselif np.linalg.norm(xnew - x) < tol:print("Converged to steady state of tolerance {} in {} steps.".format(tol,s))return x,xs x = xnew xs.append(x)print("Did not converge after {} steps (tolerance {}).".format(steps,tol))return x,xs
However, depending on the problem, this scaled learning rate may slow down convergence considerably. As an adjustment to remove this monotone decreasing learning rate, RMSprop attempts to balance the current gradient with a dampened version of the sum of squares of previous gradients from adagrad:
def rmsprop(f_p,x0,gamma=0.9,alpha=0.001,tol=1e-12,steps=1000): x = x0 xs = [x]# --------- NEW ----------- sum_sq_grad =0for s inrange(steps): sum_sq_grad = (1-gamma)*(f_p(x)**2) + gamma*sum_sq_grad v_i =-alpha*f_p(x)/np.sqrt(sum_sq_grad)# ------------------------- xnew = x + v_iif np.linalg.norm(f_p(xnew)) < tol:print("Converged to objective loss gradient below {} in {} steps.".format(tol,s))return x,xselif np.linalg.norm(xnew - x) < tol:print("Converged to steady state of tolerance {} in {} steps.".format(tol,s))return x,xs x = xnew xs.append(x)print("Did not converge after {} steps (tolerance {}).".format(steps,tol))return x,xs
This helps with the challenge of a monotone decreasing learning rate, but it introduces a dampening parameter \(\gamma\) that must be chosen (recommended values are \(\alpha = 0.001\) and \(\gamma = 0.9\)).
With momentum
Though the previous adaptive methods address the challenge of determining a learning rate, these methods are still likely to terminate in local minima. To address this issue, there are several methods which utilize a concept of “momentum” to propel iterations out of local minima with the hope of landing in the global minimum. This momentum is most simply added by incorporating previous gradients into the current update:
def gradient_descent_momentum(f_p,x0,gamma,alpha=0.01,tol=1e-12,steps=1000): x = x0 xs = [x]# --------- NEW ----------- sum_grad =0for s inrange(steps): sum_grad = f_p(x) + gamma*sum_grad v_i =-alpha*sum_grad# ------------------------- xnew = x + v_iif np.linalg.norm(f_p(xnew)) < tol:print("Converged to objective loss gradient below {} in {} steps.".format(tol,s))return x,xselif np.linalg.norm(xnew - x) < tol:print("Converged to steady state of tolerance {} in {} steps.".format(tol,s))return x,xs x = xnew xs.append(x)print("Did not converge after {} steps (tolerance {}).".format(steps,tol))return x,xs
where \(\gamma\) is a momentum parameter that determines how much of previous updates are kept for the current step (\(0 <= \gamma <=1\) where \(\gamma = 0\) includes no momentum).
However, iterations that include this momentum may jump right out of global minima and/or delay convergence. To incorporate a counterbalance to the momentum based on current success (to slow down in the right places), Nesterov acceleration adjusts the gradient according to an approximated step (to see how successful it may be in the future). By so doing, it can effectively reduce or increase the momentum according to the next future iteration:
def gradient_descent_nesterov(f_p,x0,gamma,alpha=0.01,tol=1e-12,steps=1000): x = x0 xs = [x]# --------- NEW ----------- sum_grad =0for s inrange(steps): sum_grad = f_p(x-gamma*v_i) + gamma*sum_grad v_i =-alpha*sum_grad# ------------------------- xnew = x + v_iif np.linalg.norm(f_p(xnew)) < tol:print("Converged to objective loss gradient below {} in {} steps.".format(tol,s))return x,xselif np.linalg.norm(xnew - x) < tol:print("Converged to steady state of tolerance {} in {} steps.".format(tol,s))return x,xs x = xnew xs.append(x)print("Did not converge after {} steps (tolerance {}).".format(steps,tol))return x,xs
where again \(\gamma\) is a momentum parameter. Notice that the only adjustment compared to vanilla gradient descent with momentum is calculating the gradient at an approximation of the next parameter values rather than at the current parameters.
Combining ideas
Of course, the ideas of adaptive gradients and momentum both address important issues and are not mutually exclusive, so they can both be used simultaneously (with the downside of adding more parameters to tune). Note that the adaptive step size methods (adagrad,RMSprop) use a sum of squares of the previous gradient while the momentum methods (gradient descent with momentum or Nesterov acceleration) use a sum of the previous gradient. These can be seen as the second moment and first moment of the previous gradients respectively. The Adam (adaptive moment estimation) method uses both of these gradient moments to incorporate both momentum and adaptivity:
Parameters \(\beta_1,\beta_2\) represent decay rates in incorporating previous moments into the update step.
Although this list is not comprehensive, it demonstrates the reasoning behind the common solutions for the challenges of using gradient descent in learning methods. For a more comprehensive list of recently proposed methods, see [5].
Automatic differentiation
Gradient descent methods rely on computing the gradient of the loss \(\nabla_p \mathcal{L}\) for a given parameter set \(\vec{p}^i\). For simple models, the gradients can be calculated by hand. However, for models with many nested functions and parameters (neural networks in particular) or methods whose form depends on hyperparameters, we will need an automated method. The “automatic differentiation” method is the solution to these challenges. Though it was developed for neural networks and mainly used there, its breadth of applications is only just becoming apparent. (Consider the Julia language which has worked hard to make automatic differentiation possible everywhere).
There are both “forward” and “backward” automatic differentiation methods, but we will consider only the forward which best demonstrates the “automatic” moniker. To do so, consider the first order expansion of two functions at a given point \(a\): \[
\begin{align*}
f(a + \epsilon) &= f(a) + \epsilon f'(a) \\
g(a + \epsilon) &= g(a) + \epsilon g'(a)
\end{align*}
\] where \(\epsilon\) is a small perturbation. Basic operations with these functions at this point can then be written as: \[
\begin{align*}
f + g &= [f(a) + g(a)] + \epsilon[f'(a) + g'(a)] \\
f - g &= [f(a) - g(a)] + \epsilon[f'(a) - g'(a)] \\
f \cdot g &= [f(a) \cdot g(a)] + \epsilon[f(a)\cdot g'(a) + g(a)\cdot f'(a)] \\
f \div g &= [f(a) \div g(a)] + \epsilon\left[\frac{f'(a)\cdot g(a) + f(a)\cdot g'(a)}{g(a)^2}\right]
\end{align*}
\] The derivatives are then represented by the \(\epsilon\) terms in the above equalities.
To implement this on a computer, you can create a new “Dual” number that performs additions, subtractions, multiplications, and divisions with the above operating rules (and can be extended for other functions: \(\sin,\cos,\exp\),etc).
Note
This is cumbersome and slow to implement in Python which is why large and complicated libraries have been written in C (tensorflow,pytorch,jax, and others).
It can be cleanly and easily implemented in Julia. To see a demonstration of this, see [6].
A quick demonstration of a simple implementation of this is as follows [7]. Define a dual number class:
class DualNumber:def__init__(self, val, der):self.val = valself.der = derdef__add__(self, other):ifisinstance(other, DualNumber):# DualNumber + DualNumberreturn DualNumber(self.val + other.val, self.der + other.der)else:# DualNumber + a numberreturn DualNumber(self.val + other, self.der)def__radd__(self, other):# a number + DualNumberreturnself.__add__(other)def__mul__(self,other):ifisinstance(other, DualNumber):# DualNumber * DualNumberreturn DualNumber(self.val * other.val, self.val*other.der +self.der*other.val)else:# DualNumber * a numberreturn DualNumber(self.val * other, self.der * other)def__rmul__(self,other):# a number * DualNumberreturnself.__mul__(other)def__pow__(self,power):# DualNumber ^ a numberreturn DualNumber(self.val ** power, self.der * power * (self.val ** (power -1)))def__repr__(self):# Printing a DualNumberreturn"DualNumber({},{})".format(self.val,self.der)dual1 = DualNumber(1,2); dual2 = DualNumber(3,4); other =5print(dual1,"+",dual2,"=",dual1+dual2)print(dual1,"+",other,"=",dual1+other)print(dual1,"*",dual2,"=",dual1*dual2)print(dual1,"*",other,"=",dual1*other)print(dual1,"^",2,"=",dual1**2)
This seems simple, but by using a few rules like this, we can compute simple polynomial derivatives at a point automatically. For example, computing the derivative of \(f(x) = 3x^3 + 2x + 4\) at \(x=2\). We first initialize the function and the dual number \((2,1)\) (\(1\) because the derivative of \(x\) is \(1\)).
We then pass the dual number through the polynomial function, unpack the results, and compare the result with the true derivative \(f'(2)\):
poly_dual = f(dualx)dual_val = poly_dual.val; dual_der = poly_dual.dertrue_der = df(x)print("True derivative at x=2: ", true_der)print("Dual number derivative: ", dual_der)
True derivative at x=2: 38
Dual number derivative: 38
Using the rules for dual numbers we provided above, the derivative “automatically” popped out of the polynomial evaluation.
In contrast with the forward method, the backward method keeps a log of the operations performed and then uses defined chain rules (similar to how we defined rules for the forward pass) to backtrack from the final output back to the start. This approach is more efficient when the dimension of the gradient is large and that of the output small. Forward evaluation is more efficient when the dimension of the gradient is small and that of the output large.
All in all, automatic differentiation is programming with hard-coded differential rules for primitive operations. It can also require keeping track of which operations happen and in what order. Implementing such a method relies heavily on computer science techniques since it boils down to parsing operation calls. This represents one of the clearest differences in the approaches of machine learning to those of statistics or optimization due to its computer language-heavy principles. Through this lens, machine learning could be viewed as “inferential statistics using new tools from computer science for non-traditional problems.”
Further Exploration
Each of the parameters for the gradient descent methods was chosen by hand for the above animations. Try adjusting the starting point and parameters for each to get a feel for how they behave.
Try splitting the sample data into a training and a testing set. Use the training set and one of the gradient descent methods to fit some parameters, then see how well the model generalizes to the testing set with those parameters.
Try implementing another gradient descent method from [4] using the given methods as a template.
Add the missing subtraction (__sub__, __rsub__) and division (__truediv__, __rtruediv__) methods in the DualNumber class and play around with automatically differentiation functions using those operators (you can use this as a reference). You can also make functions such as sin,cos, or exp that are meant to compute with DualNumbers.
numpy - Creating, manipulating, and operating (linear algebra, fft, etc.) on multi-dimensional arrays. A list of packages built on numpy for a variety of domains can be found on the homepage under the ECOSYSTEM heading.
scipy - Fundamentals in optimization, integration, interpolation, differential equations, statistics, signal processing, etc.
matplotlib - Static or interactive plots and animations
scikit-learn - Standard machine learning tools and algorithms built on numpy, scipy, and matplotlib
pandas - Easily represent, manipulate, and visualize structured datasets (matrices with names for columns and rows)
keras - High level neural network framework built on tensorflow
tensorflow - In depth neural network framework focused on ease and production
pytorch - In depth neural network framework focused on facilitating the path from research to production
scikit-image - Image processing algorithms and tools
Julia is a fairly new language that has been mainly proposed as an alternative to Python and Matlab, though it is general use. Its strength and its weakness is that it is “just-in-time” compiled (meaning your code is automatically analyzed and compiled just before it is run). A clever language design combined with just-in-time compilation makes Julia as clear to read and write as Python while being much faster. It can even approach the speed of C when written carefully. However, the just-in-time compilation and type system remove a chunk of the interactive convenience of Python and its young age also means that it does not have the volume of packages that Python does.
Nonetheless, it is an elegant and high-performance language to use and has shown rapid growth recently. Concise, simple, and easy to read and contribute to packages have been quickly emerging and it already provides many useful tools. As a result, it is worth describing it’s installation process, environment management, and noteable packages.
Installation
The officially supported method of installation for Julia is now using the juliaup version manager. The installer can be downloaded from the Windows store on Windows or run on MacOS or Linux with:
curl-fsSL https://install.julialang.org |sh
Environments
Julia comes with a standard environment and package manager named Pkg. Interestingly, the easiest way to use it is to run the Julia REPL (read-eval-print-loop), i.e. to run julia interactively. You can do so by typing julia into the terminal. You will then be presented with a terminal interface such as:
Type ? and hit enter to get options in this mode. We can create and activate a new environment called workshop with the command:
(@v1.8) pkg> activate --shared workshop
Note that the --shared flag will make a “global” environment that can be accessed from any directory. If we were to leave out this flag, Pkg would put a Project.toml and Manifest.toml file in the current directory that contain the name of the environment, its installed packages, and their dependencies. This can be useful to easily isolate and share environments. After running this command, our Pkg mode will have changed to represent the active environment:
(@workshop) pkg>
Installing packages
To install some packages in the active environment, write:
These packages will install and precompile. To test one of them, press backspace to leave Pkg mode and input:
julia> using Plots[ Info: Precompiling Plots [91a5bcdd-55d7-5caf-9e0b-520d859cae80]julia> x = range(0,10,100);julia> y = sin.(x);julia> plot(x,y)
This should show a plot of a simple \(\sin\) curve. Note that the precompilation of Plots took some time. However, this will not need to occur again until the package is updated. Also note that the call to plot(x,y) took some time. This is due to the just-in-time compilation. Now that the compilation has been done for inputs of the types of x and y, if you run plot(x,y) again, it should be almost instantaneous.
Cleaning up
To deactivate the environment, enter the Pkg mode again by pressing ] on an empty line, then enter:
(@workshop) pkg> activate
To delete the environment we created you can delete the environment folder at the listed location at creation. This is usually /home/user/.julia/environments/workshop on MacOS or Linux.
As compared to Python, Julia has many scientific computing tools built into its standard library. Thus, a lot of the functionality found in numpy are loaded by default. On the other hand, because of the interoperability of the language and the reduced need for a polyglot codebase (i.e. needing C and Fortran code for a Python package to be fast), packages are usually much smaller modules in Julia. For example, the functionality of the scipy package in Python can be found spread across possibly a dozen different packages in Julia. This is convenient to only load and use what you need, but inconvenient in that it may require more searching to find and the interfaces may not be standardized. The following are some packages that roughly recreate the essential Python packages here.
jupyter - Pluto.jl although you can use Julia with Jupyter via IJulia.jl
References
[1]
K. P. Murphy, Machine Learning: A Probabilistic Perspective (MIT press, 2012).
[2]
G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning, Vol. 112 (Springer, 2013).
[3]
T. Hastie, R. Tibshirani, J. H. Friedman, and J. H. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Vol. 2 (Springer, 2009).


Essi Jukkala: Portfolio
Game projects
Ketsu
Ketsu is a mobile puzzle game about symmetry and mirrored movement.
Made for Game Project (Aalto University). I did this and this and asdasd.
Plantage
Plantage is a mobile puzzle game where you must guide evolution.
- Role: Lead Programmer
- Tech: C#, Unity, Wwise
- Play online
- Download for Android, macOS, Windows
Made for Games Now! Game Jam (Aalto University). I did this and this and asdasd.
Kuplajesse
Kuplajesse is Bubble Bobble modernized!
- Role: Lead Programmer
- Tech: C#, Unity
- Play online
- Download for Android, macOS, Windows
Made for Game Project (Aalto University). I did this and this and asdasd.
Happy Teekkari, an Angry Birds clone
- Programmer
- Tech: C++, Box2D, SFML
- Download for Windows
Made for C++ Programming (Aalto University). I did this and this and asdasd.
Save the plant
- Role: Programmer, Level Designer
- Tech: C#, Unity
- Download for Android
Made for Game Project (Aalto University). I did this and this and asdasd.
Game demos
Defender-clone
- Programmer
- Tech: C#, Unity
- Play online
- Download for macOS, Windows
Made for a job interview. I did this and this and asdasd.
Asteroid defense
- Programmer
- Tech: C#, Unity
- Play online
- Download for macOS, Windows
Made for a job interview. I did this and this and asdasd.


Droom helps you find your dreamjob hassle free! Just Sign In and swipe through the opportunities right for you. No endless writing of letters or CVs; just simple.
Tap into the workforce that loves Droom. Droom is full of employees that are looking for their dream job and are expecting the hiring process of the future. Don't miss out!


Chapter 5 Data Wrangling
5.1 Intro
Data Wrangling is the process of cleaning and manipulating your data.frame. I have never obtained a dataset that did not need to be cleaned or organized in some fashion (if you have I am quite jealous).
The DataExplorer
package allows you to get a preliminary look at your data. It will check for missing data
5.2 Exploratory Data Analysis
Phase I of my “statistics” is usually termed “Data Exploration” or “Exploratory Data Analysis”
The goal of this step is to gain valuable insights through the data so that one can know what is going on with the data, which part needs to be cleaned, what new features can be built, build hypotheses to be tested during the model creation/validation phase, or even just knowing some fun facts about the data (src).
My 2 favorite packages to get a glimpse of the data are
- SmartEDA
- DataExplorer
- summarytools
- dataMaid
- janitor and here
5.3 Creating Report with DataExplorer
create_report(
df.fa, # the name of your dataframe
#y = 'heart_disease',
output_dir = 'output', # where do you want it to be saved relative to your project directory
output_file = 'data_explorer_fa_report.html', # the filename for the report
report_title = 'DTI (FA) Data Description' # the Title of your report
)
SmartEDA::ExpNumStat(tbl.desc, round = 1)
ExpNumStat(
tbl.desc,
by = "GA",
gp = "Group",
Qnt = c(.1, .9),
Outlier = TRUE,
round = 1
)
ExpNumViz(tbl.desc, target = 'Group')
summarytools::dfSummary(
tbl.desc,
varnumbers = FALSE,
round.digits = 2,
plain.ascii = FALSE,
style = "grid",
graph.magnif = .33,
valid.col = FALSE,
tmp.img.dir = "img"
)
5.3.1 Reading Material : Exploratory Data Analysis
5.4 Tasks
5.4.1 Inspecting the imported data
Among the issues you may encounter with your data is inconsistencies in columnnames. This can be rather annoying. In order to alleviate this, I like to pick one format and stick with it. When I receive a dataset from another researcher I will often use snakecase to modify their dataset accordingly.
5.4.2 Choosing good column names
- Avoid using spaces. This makes it more tedious to refer or call a column later on. In the example below our task is to rename the column “Group Level”. It should also be noted that things can get tricky when you are using certain packages and will over time, be a large burden to you. Hence, my advice is to remove spaces. Consider going into snakecase.
5.4.3 replace strings using pipes
In the code below I want to replace text in the “Test” column. Notice how I call the column once and then use the pipe operator to list the rest
5.4.4 Useful Links
Below is a list of pages I have collected that have come in very useful for beginners
<IP_ADDRESS> Selecting using the tidyverse
<IP_ADDRESS> Working with dates
We won’t cover working with dates much in this guide, because its not typical. However, should you need them the link below provides a general walkthrough. https://www.r-bloggers.com/a-comprehensive-introduction-to-handling-date-time-in-r-2/
# Adding Names to Columns ==================
# This can be required when using certain plotting functions like "likert" --> see ALPH likert4.R
names(items) <- c(
symptoms_decLOC="Did the patient have loss of consciousness?",
symptoms_headache="Did the patient have headaches?",
symptoms_nausea_vomitting="Did the patient have nausea/vomiting?",
symptoms_cranialNerve_paresis="Did the patient have cranial nerve paresis?",
symptoms_gait_disturbance="Did the patient have any gait disturbances?")
# Data Wrangling ----------------
df1$Group <- rep("Body", length(df1$`File name`))
df2$Group <- rep("Genu", length(df2$`File name`))
df3$Group <- rep("Splenium", length(df3$`File name`))
idx <- unique(df3l$`File name`) == df1$`File name`
dfx <- df1 %>%
select_if(df1$`File name` == unique(df3$`File name`))
combinedDF <- rbind(df1,df2,df3)
df1[!(df1$`File name` %in% df3$`File name`)]
idx <- setdiff(df1$`File name`, df2$`File name`)
df1 <- df1[,idx]
# Convert several columns to numeric ----------
cols <- grep(pattern = "CC_F|Left|Right", x = names(df), value = TRUE)
df[, cols] <- lapply(df[ , cols], function(x) suppressWarnings(as.numeric(x))) #supressWarnings is so you don't get "NAs introduced by coercion" in your console output
# Count the number of NA's in each column of a dataframe ----------
sapply(df, function(x) sum(is.na(x))) #this is different than "summary(df)" which gives you information on more than NA's
# Add column in specific spot ========
df <- add_column(df, "DMT_line2" = df$DMT_line, .after = "DMT_line")
# Add a blank column in a specific spot =====
items <- add_column(items, "Avg" = replicate(length(items$value.D1), NA) , .after = "value.D7")
# Remove blank rows in a specific column =======
df <- df[-which(df$start_pc == ""), ]
# Removing a column based on a string match =========
df <- filter(df, C != "Foo")
# Renaming a variable in a column : str_replace (this won't take exact strings)=============
df1$ID <- df$ID %>%
str_replace("pi6437934_2", "S2")
# Replace an exact string -------------
data$OpenBCI_FileName <- gsub("\\<y\\>","", data$OpenBCI_FileName) # replaces cols with "y" but won't touch something like "My Drive"
# if you have multiple strings to replace you can use the pipe operator to get everything done in one shot
df$ID <- df$ID %>%
str_replace("pi_14344894_9", "S9") %>% #oldstring, newstring
str_replace("pi_3478_03o4_15", "S15")
# Partial String match in a column----------
# __Method 1: str_detect ========
mtcars %>%
filter(str_detect(rowname, "Merc")) # this will filter everything that has the pattern "Merc" in the column
# __Method 2 : grepl ==========
dplyr::filter(mtcars, !grepl('Toyota|Mazda', type)) # Using grepl
cols <- grep(pattern = "symptoms|Previous_shunt", x = names(df), value = TRUE) #Select columns that start with symptoms
df[cols] <- as.data.frame(lapply(df[cols],function(x) {factor(x,levels=mylevels, exclude="")}))
#__Method 3 : grep (similar to grepl) =====
cols <- grep(pattern = "symptoms", x = names(df2), value = TRUE) #Select columns that start with symptoms
# Renaming a column ================
df <- df %>%
df <- df %>%
dplyr::rename(
#‘New Column Name’= ‘Old Name’
"month"= "Month"
)
# Removing NA's-----------
# __Method 1: is.na() ====
df <- df %>%
filter(!is.na(columnName)) # give me a new dataframe with only the international services.
# __Method 2: na.omit() ====
df1 <- na.omit(df1)
# Filtering our data that fits a condition: filter=========
new_df <- filter(df, service=="International") # give me a new dataframe with only the international services.
To rename parts of a column using %>%
you need to use mutate
5.5 For Item-based data
The likert
package seems great. I have used it to great success in past projects.


About Me
I'm a young man with 19 years old, I'm graduating in software engineering at the University of Ribeirão Preto.
I started in the world of Information technology at the age of 15, when I entered the technical computer course at ETEC Prof. Alcídio de Souza Prado in Orlândia.
I currently have basic and intermediate knowledge in some languages and development tools, such as C, JavaScript, PHP, VB, Java, HTML, CSS, VB, BootStrap, WordPress and others.
I'm studying new languages, such as, C# and .NET, React, Angular and TypeScript to be aligned with the market.
I'm open to opportunities for Front-End, Back-End or FullStack development.
Hobies
bicycling | gaming | soccer | photography
Name: André Otávio Bordonal Artuzo
Date of birth: 26-Sep-2001
Nationality: Sales Oliveira - SP - Brazil
+55 (16)98212-2502
Skills
Education
Universidade de Ribeirão Preto
Software Engineering
2019-2022
ETEC Prof. Alcidio De Souza Prado
Computer technical course
2016-2018
ACBEU Inglês e Espanhol
Basic to intermediate english course
2013-2018
Work Experience
Morlan SA
Service Desk Intern
Dez/2020 - Present
CRWA Agência Digital
Marketing and development intern
Jan/2020 - Jun/2020
INFOVIP Tecnologia
Service Desk Intern
Mar/2019 - Oct/2019


the lesson
To get started, set up your local configuration by
selecting the audio input(s).
Then connect to your peer. That's pretty much
it!
You can control the volume for individual audio streams from
your computer as well
as your peers using the audio controls.
Enjoy!
setup your configuration
Select audio inputs.
You may have to play around with the available inputs to select the optimal ones
connect and go live!
Warning!
Error!!


Class Document
Class Document
- Tokamak\Dom\Node
- Tokamak\Dom\Document
Direct known subclasses
Tokamak\Dom\HTMLDocument
public
|
#
__construct( array $data = null, array $doctype = null )
Initialize the document by passing it data/state. |
public
|
|
public
|
|
public
string
|
|
abstract public
string
|
addDomNode(),
appendComponent(),
appendElement(),
getDom(),
getDomNode(),
hasDomNodes(),
render(),
setDom()
|
public static
string
|
$version
The XML version of the document. Not used in HTML5 docs. |
#
'1.0'
|
public static
string
|
$encoding
The character encoding of the document. |
#
'UTF-8'
|
protected
DOMDocument
|
$dom
The underlying DOMDocument instance over which this library provides an abstraction. |
$COMPONENT_NAMESPACE,
$domNodes
|


Before Change
:param batch_size: Size of the batch on which adversarial samples are generated.
super(CarliniLInfMethod, self).__init__(classifier)
if not isinstance(classifier, ClassifierGradients):
raise ClassifierError(self.__class__, [ClassifierGradients], classifier)
kwargs = {
"confidence": confidence,
"targeted": targeted,
"learning_rate": learning_rate,
"max_iter": max_iter,
"max_halving": max_halving,
"max_doubling": max_doubling,
"eps": eps,
"batch_size": batch_size,
}
assert self.set_params(**kwargs)
// There is one internal hyperparameter:
// Smooth arguments of arctanh by multiplying with this constant to avoid division by zero:
self._tanh_smoother = 0.999999
After Change
]
def __init__(
self,
classifier: ClassifierGradients,
confidence: float = 0.0,
targeted: bool = False,
learning_rate: float = 0.01,
max_iter: int = 10,
max_halving: int = 5,
max_doubling: int = 5,
eps: float = 0.3,
batch_size: int = 128,
) -> None:
Create a Carlini L_Inf attack instance.
:param classifier: A trained classifier.
:param confidence: Confidence of adversarial examples: a higher value produces examples that are farther away,
from the original input, but classified with higher confidence as the target class.
:param targeted: Should the attack target one specific class.
:param learning_rate: The initial learning rate for the attack algorithm. Smaller values produce better
results but are slower to converge.
:param max_iter: The maximum number of iterations.
:param max_halving: Maximum number of halving steps in the line search optimization.
:param max_doubling: Maximum number of doubling steps in the line search optimization.
:param eps: An upper bound for the L_0 norm of the adversarial perturbation.
:param batch_size: Size of the batch on which adversarial samples are generated.
super(CarliniLInfMethod, self).__init__(classifier)
self.confidence = confidence
self.targeted = targeted
self.learning_rate = learning_rate
self.max_iter = max_iter
self.max_halving = max_halving
self.max_doubling = max_doubling
self.eps = eps
self.batch_size = batch_size
self._check_params()
// There is one internal hyperparameter:
// Smooth arguments of arctanh by multiplying with this constant to avoid division by zero:
self._tanh_smoother = 0.999999


nips nips2003 nips2003-157 nips2003-157-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Peter Dayan, Michael Häusser, Michael London
Abstract: Computational mysteries surround the kernels relating the magnitude and sign of changes in efficacy as a function of the time difference between pre- and post-synaptic activity at a synapse. One important idea34 is that kernels result from filtering, ie an attempt by synapses to eliminate noise corrupting learning. This idea has hitherto been applied to trace learning rules; we apply it to experimentally-defined kernels, using it to reverse-engineer assumed signal statistics. We also extend it to consider the additional goal for filtering of weighting learning according to statistical surprise, as in the Z-score transform. This provides a fresh view of observed kernels and can lead to different, and more natural, signal statistics.


| 11:00am-11:50am | 12:00pm-12:50pm | 1:00pm-1:50pm | 2:00pm-12:50pm | 3:00pm-3:50pm | |
|---|---|---|---|---|---|
| Monday | Programming for Digital Forensics | Academic Study Skills | Break | Computer Orginization | Web & Distributed Programming |
| Tuesday | Intro to Networking | Intro to Networking | |||
| Wednesday | Programming for Digital Forensics | Academic Study Skills | Break | Computer Orginization | Web & Distributed Programming |
| Thursday | Intro to Networking | Intro to Networking | |||
| Friday | Programming for Digital Forensics | Academic Study Skills | Break | Computer Orginization | Web & Distributed Programming |


Hello, world!
Lorem Ipsum is simply dummy text of the printing and typesetting industry.
Lorem Ipsum is simply dummy text of the printing and typesetting industry.
TEST
TEST 2
HOME
ABOUT US
CONTACT US


Listen to Beck’s dreamy new song, ‘Dark Places’ from upcoming new album, ‘Hyperspace’
The album is out later this month
Beck has shared a dreamy new track, ‘Dark Places’, from his upcoming new album. You can listen to the new track below.
- Read More: Beck ‘Colours’ Review
The atmospheric song will appear on Beck’s new album, ‘Hyperspace’ which he teased the release of earlier this month via a cryptic Instagram post.
Seven of the album’s 11 tracks, including the previously released ‘Saw Lightning’, were co-written and co-produced by Pharrell. Another previously released track, ‘Uneventful Days’ was accompanied by a video directed by Dev Hynes.
‘Hyperspace’ comes two years after his last record, 2017’s Grammy award-winning ‘Colors’ and is set for release on November 22 via Capitol Records. You can listen to ‘Dark Places’ here:
‘Hyperlife’ tracklist:
1. ‘Hyperlife’
2. ‘Uneventful Days’
3. ‘Saw Lightning’
4. ‘Die Waiting’
5. ‘Chemical’
6. ‘See Through’
7. ‘Hyperspace’
8. ‘Stratosphere’
9. ‘Dark Places’
10. ‘Star’
11. ‘Everlasting Nothing’
Speaking about his collaboration with Pharrell to NME, Beck said: “I’d always wanted to make a record with him. We had been friendly over the years and had got together and talked about making some music back in 2012, but around that time he ended up putting out a song with Daft Punk, then ‘Blurred Lines’ and ‘Happy’.”
They started collaborating when Pharrell asked Beck to work on the new N.E.R.D. record. “From there we started sharing songs and writing and doing this project together,” Beck explained.
“I had a few things after the ‘Colors’ tour finished and I really wanted to seize the moment with this project,” Beck added. “There’d been a period of time where Pharrell had been really busy and an opportunity opened up so I went with it. Originally it was going to be a single or an EP, but I think we were both surprised when there was a body of work here.”
The upcoming will album also feature Coldplay’s Chris Martin on ‘Stratosphere’ and Sky Ferreira on ‘Die Waiting’.
Back in October, Martin joined Beck onstage for a rendition of his 1994 cult hit, ‘Loser’.


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From: Mrs. Mary Kobe, 17 Stuttgart Avenue, Randburg, Johannesburg, Republic of South Africa. Dear Sir, VERY CONFIDENTIAL PLEASE I have been waiting for the Military to hand-over government of Sierra Leone to the Civillian. I am Mrs. Mary Kobe, widow of late General John Kobe. I procured your contact information from my search for a reliable person who is capable of handling such a proposal of such a magnitude, as my late husband has made it that this proposal herein will be at the betterment of his family, as the person to be the foreign partner will take good care of his investment and family as well. My late husband was one of the victims of the Civil war. I have been informed by one of our family's attorney who is a South African citizen with the name Barrister Barrister cliff Alexaandern that my late husband operated a secret account with a fictitious name in the FIRST NATIONAL BANK here in Johannesburg. In this account, he has (Thirty-nine Million Dollars) $39,000,000 only, which was credited in his favour. In my husband's 'WILL' he stated that one of his partners overseas will take care of his family/properties. The attorney is waiting for my late husband's partner overseas who is to take care of his family/properties according to his WILL. Since no mention was made in the WILL as to who the said partner is, I want you to take advantage of this upon receipt of this email, you are to contact the family attorney with the following information: Name: Barrister cliff Alexaander. Address: 17 Brixton Street, Orange Groove, Johannesburg. RSA> Phone: +27=83-540-6943. Fax: +27-86-640-4824. Email: cliffattorneys<EMAIL_ADDRESS>Try to contact this lawyer as urgent as possible with the following reference: Amount deposited: $39,000,000 US Dollar. Bank: First National Bank of South Africa. Month/Year of Account: July, 1999. Secret Code: 43/99FNB/MBW/13.00131/7. It has been resolved by the family that 20% of the total fund shall be your share for presenting yourself for this purpose/assistance you will give in this regard,while 10% mapped-out for the re-enbursement of all local and international expenses, which will be incurred in the transfer process. Finally the remaining 70% is for myself and my children, which a good part of this fund shall be directed towards executing my husband's WILL, which is to buy shares and stocks in foriegn countries, in order to secure our children's future. To facilitate this transaction, please send your telephone and fax number, throough which you will be contacted immediately by our attorney, which his information is written above. Note: This is our life and I trust you with it. I want you to reciprocate the same trust, which I repose in you. Give this matter a prompt and quick response. Please, direct your response VIA to our family attorney whom the information is written above and discuss with him towards the effective conpletion of this transaction/how to get this fund out of the bank. My assurance to you is that this transaction is hundred percent risk-free. Best regards, Mrs. Mary Kobe.


Our favorite things
These are a few of our favorite things
People
The First lady
Michelle Obama
Flavor of Ice-cream
Mint Choc chip
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Name: |
Name: |
|
Details:
A miscellaneous event associated with this event.
|
||
|
«ABIE» Party Class |
Name: |
Name: Receiver Party |
|
Details:
The party receiving this document.
|
||
|
«ABIE» Period Class |
Name: |
Name: |
|
Details:
The period during which the event takes place.
|
||
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«ABIE» Promotional Event Class |
Name: |
Name: |
|
Details:
The description of a promotional event associated with this event.
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||
|
«ABIE» Supplier Party Class |
Name: |
Name: Seller Supplier Party |
|
Details:
The seller.
|
||
|
«ABIE» Signature Class |
Name: |
Name: |
|
Details:
A signature applied to this document.
|


Before Change
// "1,2,3," - splitting this by the separator we should get ["1", "2", "3", ""] - always an extra
// empty string if the separator is properly always a suffix and not applied just between
// entries.
self.assertEqual(
sorted(list(output) + [""]), sorted((self.execute_rule(**kwargs)).split(sep))
)
def assert_console_output(self, *output, **kwargs):
Verifies the expected output entries are emitted by the console task under test.
After Change
// expect: "1,2,3," - splitting this by the separator we should get
// ["1", "2", "3", ""] - always an extra empty string if the separator is properly always
// a suffix and not applied just between entries.
result = self.execute_rule(**kwargs)
assert sorted([*output, ""]) == sorted(result.stdout.split(sep))
def assert_console_output(self, *output, **kwargs) -> None:
Verifies the expected output entries are emitted by the goal rule.


assigned
by Trey Pendragon at 2020-09-21 21:03:01.referenced
by Christina Chortaria at 2020-09-21 15:41:01.merged
by Christina Chortaria at 2020-09-21 15:41:01.closed
by Christina Chortaria at 2020-09-21 15:41:01.head_ref_deleted
by Christina Chortaria at 2020-09-21 15:41:02.referenced
by None at 2020-09-21 15:56:26.merged
by None at 2020-09-21 15:56:26.closed
by None at 2020-09-21 15:56:26.head_ref_deleted
by None at 2020-09-21 15:56:27.ready_for_review
by Trey Pendragon at 2020-09-21 21:55:58.moved_columns_in_project
by Trey Pendragon at 2020-09-21 22:45:01.moved_columns_in_project
by James R. Griffin III at 2020-09-21 17:46:14.unassigned
by James R. Griffin III at 2020-09-21 17:47:22.assigned
by None at 2020-09-21 15:24:00.referenced
by None at 2020-09-21 15:25:03.closed
by None at 2020-09-21 15:47:29.Coverage increased (+0.02%) to 80.896% when pulling 8b9531e2df377fa624e85df1f53992750d7ccc6e on campus-access-case into f8b670c90bddcecced4e7b9dc3a044837f4fcf73 on master.
deployed
by Trey Pendragon at 2020-09-21 15:34:30.referenced
by None at 2020-09-21 15:47:29.merged
by None at 2020-09-21 15:47:29.closed
by None at 2020-09-21 15:47:29.head_ref_deleted
by None at 2020-09-21 15:47:36.closed
by None at 2020-09-21 12:58:29.referenced
by None at 2020-09-21 12:58:28.merged
by None at 2020-09-21 12:58:28.closed
by None at 2020-09-21 12:58:29.head_ref_deleted
by None at 2020-09-21 12:58:33.We did this, right? And just forgot to close the issue? Closing it now.
closed
by None at 2020-09-21 14:35:48.referenced
by None at 2020-09-21 15:40:23.merged
by None at 2020-09-21 15:40:23.closed
by None at 2020-09-21 15:40:23.head_ref_deleted
by None at 2020-09-21 15:40:27.assigned
by Christina Chortaria at 2020-09-21 13:36:35.moved_columns_in_project
by Christina Chortaria at 2020-09-21 13:41:20.referenced
by Christina Chortaria at 2020-09-21 14:09:25.referenced
by Christina Chortaria at 2020-09-21 14:43:08.moved_columns_in_project
by Christina Chortaria at 2020-09-21 14:43:37.closed
by Christina Chortaria at 2020-09-21 16:12:07.moved_columns_in_project
by Christina Chortaria at 2020-09-21 16:12:08.@hackmastera thanks, can you check again?
Coverage increased (+0.2%) to 96.75% when pulling 687d8ecf720a6ad5dba692e31e0fb791f6fc2419 on 1276-clean-lines-applicationcss into 564e8dce18ea7eec02d8ee1ad479687682a01695 on main.
added_to_project
by Christina Chortaria at 2020-09-21 15:05:23.labeled
by Christina Chortaria at 2020-09-21 15:06:38.deployed
by Trey Pendragon at 2020-09-21 15:32:52.mentioned
by None at 2020-09-21 15:37:28.subscribed
by None at 2020-09-21 15:37:28.review_requested
by Christina Chortaria at 2020-09-21 15:37:36.referenced
by Christina Chortaria at 2020-09-21 16:12:07.merged
by Christina Chortaria at 2020-09-21 16:12:07.closed
by Christina Chortaria at 2020-09-21 16:12:08.moved_columns_in_project
by Christina Chortaria at 2020-09-21 16:12:08.@dependabot rebase
@dependabot rebase
Coverage remained the same at 96.578% when pulling 604bea69c22d1262a4c1f26b1c6b59e117f1c694 on dependabot/npmandyarn/acorn-5.7.4 into ec3f82c2a70da4e00b361c4e130c768c6d611b50 on main.
mentioned
by Dependabot at 2020-09-21 15:42:11.head_ref_force_pushed
by None at 2020-09-21 15:42:55.mentioned
by Dependabot at 2020-09-21 16:47:06.head_ref_force_pushed
by None at 2020-09-21 16:47:38.deployed
by Trey Pendragon at 2020-09-21 16:48:46.referenced
by Christina Chortaria at 2020-09-21 17:13:10.merged
by Christina Chortaria at 2020-09-21 17:13:10.closed
by Christina Chortaria at 2020-09-21 17:13:10.moved_columns_in_project
by Christina Chortaria at 2020-09-21 17:13:11.head_ref_deleted
by None at 2020-09-21 17:13:18.renamed
by Kevin Reiss at 2020-09-21 17:17:42.labeled
by Kevin Reiss at 2020-09-21 17:18:41.moved_columns_in_project
by James R. Griffin III at 2020-09-21 17:45:41.unassigned
by James R. Griffin III at 2020-09-21 17:47:09.closed
by None at 2020-09-21 17:47:19.Related to #1995; closing both of these since we are using a different strategy now.
closed
by None at 2020-09-21 17:47:59.Coverage remained the same at 96.75% when pulling 9c331c674e8ab80f404c84694992145537bc168d on lando_db into 970c3da8baecfe27c8128dca54999e5ce857f6db on main.
review_requested
by Christina Chortaria at 2020-09-21 13:03:28.deployed
by Trey Pendragon at 2020-09-21 17:08:16.referenced
by Christina Chortaria at 2020-09-21 17:49:27.merged
by Christina Chortaria at 2020-09-21 17:49:27.closed
by Christina Chortaria at 2020-09-21 17:49:27.Initial implementation is in place. Other issues track related bugs, loose ends, and are labeled as such.
closed
by None at 2020-09-21 17:50:52.Coverage remained the same at 96.578% when pulling e1d4246f91b33187a6c59fdd36271bbc217ec151 on dependabot/npmandyarn/websocket-extensions-0.1.4 into 8867f1326d2c85a35bf60d39d39ec4840e5dcbbc on main.
@dependabot rebase
mentioned
by Dependabot at 2020-09-21 17:50:43.head_ref_force_pushed
by None at 2020-09-21 17:51:14.deployed
by Trey Pendragon at 2020-09-21 18:05:48.referenced
by Christina Chortaria at 2020-09-21 19:09:52.merged
by Christina Chortaria at 2020-09-21 19:09:52.closed
by Christina Chortaria at 2020-09-21 19:09:52.moved_columns_in_project
by Christina Chortaria at 2020-09-21 19:09:53.head_ref_deleted
by None at 2020-09-21 19:10:03.@hackmastera is this still an issue for you? I tested on chrome, firefox, safari and brave and am not seeing this issue.
mentioned
by None at 2020-09-21 19:18:05.subscribed
by None at 2020-09-21 19:18:05.referenced
by None at 2020-09-21 19:33:39.@dependabot rebase
Superseded by #2264.
mentioned
by Dependabot at 2020-09-21 19:20:02.closed
by None at 2020-09-21 19:21:22.moved_columns_in_project
by None at 2020-09-21 19:21:23.head_ref_deleted
by None at 2020-09-21 19:21:24.Coverage remained the same at 96.578% when pulling 78dd60ceb871961cddd54b0c43ed1197bdd8e5ec on dependabot/npmandyarn/lodash-4.17.20 into a9a1440acf50ca72f9ee12265a123a7fcd6e14ae on main.
labeled
by None at 2020-09-21 19:21:20.labeled
by None at 2020-09-21 19:21:20.Coverage remained the same at 96.578% when pulling b8fe8417f2dd2db7ca77b557ab46bc85c3d230d6 on search-form-height into a9a1440acf50ca72f9ee12265a123a7fcd6e14ae on main.
head_ref_force_pushed
by None at 2020-09-21 19:33:40.deployed
by Trey Pendragon at 2020-09-21 20:12:09.referenced
by Christina Chortaria at 2020-09-21 20:44:47.merged
by Christina Chortaria at 2020-09-21 20:44:48.closed
by Christina Chortaria at 2020-09-21 20:44:48.added_to_project
by Christina Chortaria at 2020-09-21 15:09:55.assigned
by Christina Chortaria at 2020-09-21 15:09:57.moved_columns_in_project
by Christina Chortaria at 2020-09-21 15:10:20.renamed
by Christina Chortaria at 2020-09-21 21:28:05.referenced
by Christina Chortaria at 2020-09-21 22:26:19.referenced
by Christina Chortaria at 2020-09-22 00:10:57.Coverage remained the same at 96.578% when pulling b29cb969b7592efb2d3f865543e00efe2b360b6f on dependabot/npmandyarn/elliptic-6.5.3 into 344d2a30d2a69e90edd3cf5a2dda15f815038db6 on main.
@dependabot rebase
mentioned
by Dependabot at 2020-09-21 21:29:28.head_ref_force_pushed
by None at 2020-09-21 21:29:59.deployed
by Trey Pendragon at 2020-09-21 22:07:18.referenced
by Christina Chortaria at 2020-09-21 22:16:34.merged
by Christina Chortaria at 2020-09-21 22:16:34.closed
by Christina Chortaria at 2020-09-21 22:16:34.moved_columns_in_project
by Christina Chortaria at 2020-09-21 22:16:34.head_ref_deleted
by None at 2020-09-21 22:16:44.assigned
by Christina Chortaria at 2020-09-21 22:44:15.referenced
by Christina Chortaria at 2020-09-22 00:05:21.referenced
by Christina Chortaria at 2020-09-22 00:19:21.closed
by Trey Pendragon at 2020-09-22 01:28:46.Coverage remained the same at 96.578% when pulling e6d861c235780de50832386d3f69b0834ab9d72c on 2266-update-browslists-port into 5159749be4bae9d6e38523dcadd15ce38a8a54a7 on main.
referenced
by Trey Pendragon at 2020-09-22 01:28:46.merged
by Trey Pendragon at 2020-09-22 01:28:46.closed
by Trey Pendragon at 2020-09-22 01:28:46.head_ref_deleted
by Trey Pendragon at 2020-09-22 01:28:50.added_to_project
by None at 2020-09-21 15:00:25.moved_columns_in_project
by None at 2020-09-21 15:00:29.assigned
by None at 2020-09-21 15:00:31.referenced
by None at 2020-09-21 15:14:34.closed
by Christina Chortaria at 2020-09-21 15:20:40.moved_columns_in_project
by Christina Chortaria at 2020-09-21 15:20:41.Coverage remained the same at 100.0% when pulling 48b2ba7c9f8615c727e92206be962b1eaf153d12 on 832-deploy-main into 7c3eb3a78c0742c99d72a8162dc17ef7f4d80120 on main.
referenced
by Christina Chortaria at 2020-09-21 15:20:40.merged
by Christina Chortaria at 2020-09-21 15:20:40.closed
by Christina Chortaria at 2020-09-21 15:20:40.Created #834 #835 #836 and #837 and gave them a 'maintenance' label. Closing this issue in favor of those.
assigned
by None at 2020-09-21 14:20:28.moved_columns_in_project
by None at 2020-09-21 14:20:39.closed
by None at 2020-09-21 15:53:51.moved_columns_in_project
by None at 2020-09-21 15:53:52.labeled
by None at 2020-09-21 15:35:54.added_to_project
by None at 2020-09-21 15:55:13.assigned
by None at 2020-09-21 17:27:55.moved_columns_in_project
by None at 2020-09-21 17:27:59.referenced
by None at 2020-09-21 18:44:31.Coverage remained the same at 100.0% when pulling 5b83627bf241c50330592565a447207bb47e8743 on 834-deprecation into acc52148518f07b637827ee19b6db1dd24dc9b93 on main.
labeled
by None at 2020-09-21 15:38:47.added_to_project
by None at 2020-09-21 15:55:16.assigned
by None at 2020-09-21 19:07:45.moved_columns_in_project
by None at 2020-09-21 19:07:48.referenced
by None at 2020-09-21 19:08:42.labeled
by None at 2020-09-21 15:50:20.added_to_project
by None at 2020-09-21 15:55:11.assigned
by None at 2020-09-21 19:09:56.moved_columns_in_project
by None at 2020-09-21 19:09:59.referenced
by None at 2020-09-21 19:57:06.Coverage remained the same at 100.0% when pulling 2c1f4b639c41ac5de59183c74963e3efb986f613 on 835-expectations-on-nil into acc52148518f07b637827ee19b6db1dd24dc9b93 on main.
I created the carousel on https://dpul-staging.princeton.edu/cotsen/home.collection-home in order to test this PR.
deployed
by Trey Pendragon at 2020-09-21 19:59:20.Upstream discussion is at https://github.com/projectblacklight/blacklight/issues/1718
Not sure what the use case is for any particular choice here. Blacklight 7 will default to just the id field. Any reason we would need more than that? Perhaps ignore this deprecation until upgrading to a Blacklight 7 version of spotlight.
labeled
by None at 2020-09-21 15:51:46.added_to_project
by None at 2020-09-21 15:55:08.closed
by None at 2020-09-21 20:20:10.moved_columns_in_project
by None at 2020-09-21 20:20:10.waiting for feedback from OIT wrt the wrong CN name.
unassigned
by James R. Griffin III at 2020-09-21 17:44:45.moved_columns_in_project
by James R. Griffin III at 2020-09-21 17:45:45.referenced
by Eliot Jordan at 2020-09-21 20:35:44.merged
by Eliot Jordan at 2020-09-21 20:35:44.closed
by Eliot Jordan at 2020-09-21 20:35:44.head_ref_deleted
by Eliot Jordan at 2020-09-21 20:35:45.referenced
by Francis Kayiwa at 2020-09-21 22:16:27.merged
by Francis Kayiwa at 2020-09-21 22:16:27.closed
by Francis Kayiwa at 2020-09-21 22:16:27.head_ref_deleted
by Francis Kayiwa at 2020-09-21 22:16:28.@kayiwa Added a test and got rid of that file. Looks like maybe that molecule generation task makes it?
head_ref_force_pushed
by Trey Pendragon at 2020-09-21 21:54:51.head_ref_force_pushed
by Trey Pendragon at 2020-09-21 22:54:01.head_ref_force_pushed
by Trey Pendragon at 2020-09-21 22:58:56.mentioned
by Francis Kayiwa at 2020-09-21 22:59:15.subscribed
by Francis Kayiwa at 2020-09-21 22:59:15.referenced
by Christina Chortaria at 2020-09-21 15:49:13.closed
by Eliot Jordan at 2020-09-21 15:52:49.referenced
by Eliot Jordan at 2020-09-21 15:52:50.referenced
by Eliot Jordan at 2020-09-21 15:52:48.merged
by Eliot Jordan at 2020-09-21 15:52:49.closed
by Eliot Jordan at 2020-09-21 15:52:49.head_ref_deleted
by Eliot Jordan at 2020-09-21 15:52:52.unassigned
by James R. Griffin III at 2020-09-21 17:44:25.moved_columns_in_project
by James R. Griffin III at 2020-09-21 17:45:52.renamed
by Eliot Jordan at 2020-09-21 17:52:25.added_to_project
by Eliot Jordan at 2020-09-21 18:19:26.moved_columns_in_project
by Eliot Jordan at 2020-09-21 18:19:31.added_to_project
by Eliot Jordan at 2020-09-21 18:22:39.added_to_project
by Eliot Jordan at 2020-09-21 18:57:58.moved_columns_in_project
by Eliot Jordan at 2020-09-21 19:16:23.assigned
by Eliot Jordan at 2020-09-21 19:16:37.moved_columns_in_project
by Eliot Jordan at 2020-09-21 20:13:58.labeled
by Eliot Jordan at 2020-09-21 20:02:50.labeled
by Eliot Jordan at 2020-09-21 20:02:56.assigned
by Eliot Jordan at 2020-09-21 20:17:54.added_to_project
by Eliot Jordan at 2020-09-21 20:17:28.moved_columns_in_project
by Eliot Jordan at 2020-09-21 20:17:31.assigned
by Eliot Jordan at 2020-09-21 20:18:06.Coverage remained the same at 100.0% when pulling 79f24544fa590c549fa9db4c4bd13734703a9f84 on 891-dependabot into b63c8feb08a5b6f599841ad83df423880f0bc6a8 on master.
assigned
by Eliot Jordan at 2020-09-21 21:09:52.closed
by None at 2020-09-21 12:12:09.Pickup and in library use should fail silently to begin with.
assigned
by None at 2020-09-21 12:26:58.


<%sys_longname%>
Logout
Home
User 1
User 2
Maintenance
Overview
Update
Statistic
Time
Network
Users
<%sys_shortname%>
Network
Network setup
Obtain IP address automatically
Set IP address manually
MAC
<%sys_ip_mac%>
Local IP address
Network mask
Gateway IP address
Preferred DNS server
Alternate DNS server
Syslog server
Enable TNE Setup
Enable
Reboot
Cancel


Field of visualItem of kind node,
used for saving a label of the node consisting of
to numbers "a/b" with "a" being the number of
coreferent feature vectors associated with that node
and "b" the number of disreferent feature vectors.
Field of visualItem of kind node,
actually there are as many fields for the weights
as there are features in the input space,
each one has as its name nodeWeight + i,
i being the ID of the feature.


Fight Club (1999) Brad Pitt, Edward Norton, Helena Bonham Carter, Meat Loaf Aday, Jared Leto. Music by the Dust Brothers. Edited by James Haygood. Cinematography by Jeff Cronenweth. Screenplay by Jim Uhls, based on the novel by Chuck Palahniuk. Directed by David Fincher. 139 minutes. Rated R, 4 stars (out of five stars)
Review by Ed Johnson-Ott, NUVO Newsweekly www.nuvo-online.com Archive reviews at http://us.imdb.com/ReviewsBy?Edward+Johnson-Ott To receive reviews by e-mail at no charge, send subscription requests to<EMAIL_ADDRESS>At one point in "Fight Club," Edward Norton and Brad Pitt study a designer underwear ad posted on the wall of a bus. After looking at images of a lean, incredibly well defined male body, Norton asks "Is that what a man looks like?" and the pair break into derisive laughter. In another segment, Pitt lectures a group of men, stating they were betrayed by a television culture that told each boy he was destined to be a rich, celebrated rock star or movie star. The irony, of course, is that Norton and Pitt are both rich, celebrated movie stars with bodies that closely resemble those in the underwear ad. The beauty of "Fight Club" is that its creators are completely aware of the irony.
Dark, brutal and very funny, "Fight Club," the latest from David Fincher ("Alien3," "Seven" and "The Game"), is one of the most mesmerizing films to come along in quite a while.   in a crowded theater, knowing that most of us will stay and study the flames instead of running to safety.
 Then we jump backwards in time to learn what led to this moment. Frazzled by insomnia, the narrator begs a doctor for medication to help him sleep. Instead, the physician snaps at his patient, suggesting he visit a weekly testicular cancer victims' group to "see what real suffering is like." At the meeting, the narrator finds himself sobbing in the arms of "fellow" sufferers, providing the catharsis he needs to sleep. He quickly becomes a support group addict, attending different meetings daily under a variety of assumed names.
All is well until he meets Marla Singer (Helena Bonham Carter), another poseur who travels the support group circuit because it's "cheaper than a movie and there's free coffee.'' The narrator cannot cry with another "tourist" in the room and his insomnia soon returns.
Then, during a plane flight, he meets Tyler Durden (Brad Pitt), a self-assured provocateur who works two part-time jobs when not peddling his home-made soap to upscale stores. As a projectionist, he splices single pornographic frames into family films. As a waiter, he "seasons" foods with his bodily fluids. Tyler and the narrator bond during a night of drinking, sealing their newfound friendship with a just-for-the-hell-of-it fistfight.  Other men witness the sparring and, in short order, Fight Club is born, an underground phenomena that spreads like wildfire.
Tyler becomes the central figure of the new men's movement, spouting bumper sticker philosophy to his rapt audience of lost boys desperate to remove the numbness from their ordered lives. Tyler begins assigning homework ("Pick a fight with a stranger. Lose the fight") and Fight Club escalates into Project Mayhem, something far more frightening. The narrator is shaken by the changes. Why is Tyler getting all the credit for something they both created? Why is Marla sleeping with Tyler? And why didn't Tyler tell him about Project Mayhem?
The extremely violent madness spins wildly, with kinetic camerawork and the Dust Brothers' score creating a surreal atmosphere of impending doom. The cast is outstanding, with the three principal actors doing flawless work and Meat Loaf Aday contributing a solid supporting turn. Jim Uhls' screenplay, based on the novel by Chuck Palahniuk, contains so many ideas that it is impossible for Fincher to address them all, so the director simply presents them for our consideration. While sifting through the barrage, certain things linger, from the early, almost subliminal single-frame flashes of Tyler to the overwhelming homoerotic imagery in the bastions of hyper-machismo. Of course, Tyler's psychobabble is merely a rationalization for stylish violence (evoking memories of "A Clockwork Orange"), but what does Fincher really think about the subject?
The moral conclusions are left up to us. In interviews, Fincher merely stresses that the film is primarily a comedy. And it is: a grand, horrific comedy that actually provokes discussion and reflection.
In light of a major revelation that comes late in the film, you'll likely go see "Fight Club" again. I did, and the story is even more fascinating the second time around. But as I left the theater, yet another question popped into my head that will send me back to study "Fight Club" a third time. SPOILER ALERT: THE FOLLOWING MAY REVEAL A SIGNIFICANT PLOT POINT. After you learn the secret, ask yourself "Is Marla Real?" Chew on that one for awhile.
© 1999 Ed Johnson-Ott
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Useful little things...
During the development of XData, some helpers were developed that may be useful to You too...
Single value IEnumerable
When it is necessary to call the method that requires IEnumerable<T> and You have a single value, the AsEnum method will be useful.
Extensions for collections
To clear List with a preliminary call of some logic to free resources associated with a sheet element, the Clear method will help.
The GetOrAdd method for List will also be useful.
When you need to compare two Dictionary instances, the HasSameValuesAs method can help.
For ConcurrentDictionary, the RemoveAll method may come in handy.
Outer join
For those who are familiar with SQL, it is often convenient to use the concept of OuterJoin in the union of sets, and how it is implemented in LINQ is not comfortable to use. XData has a built-in mechanism for combining sets using methods:
Tip
It is necessary to take into account that these procedures use the Linq2object interface IEnumerable and for the effective use of combining data in the database, you must use mapping or dynamic queries.
Processing
To pre-process an object before using it (for example, to initialize the properties of a repository before using it as a LINQ data source), it is useful to use the Do extension:
var obj = dataScope.GetRepository<Invoice>()
.Do(x => x.Instance.GetChild(z => z.InvoiceSpec))
.First();
To access source object use property Instance of action argument and properties of dynamic property State of action argument to store some values between actions.


My random stuff:
hopalongs
- An animation showing what Barry Martin Attractors look like.
planets
- Real time Newtonian gravity simulation.
kaleidoscopes
- Observe patterns everywhere.
langton
- Program your own
Langton Ant
.
percolation
- How water flows through porous substances. Or something.
graph epidemic
- How disease spreads in your neighborhood. I was
not alone
.
l-system
- Fractals described purely in terms self similarity. May look bit
synthetic
.
mining stuff
- Resource packs for minecraft. More of tools than decoration.
about
- Who am I?


SoftUni Projects
I've contributed in 11 different projects with different teams, using different technologies. Teamwork projects are great way to learn working in team and study fast new things.
Projects and Contribution
- The very first! Advanced C# September 2015 - Jumping Dude - functionality of the game
- Teamwork and Personal Skills - September 2015 - League of Legends article - translating from english and writing article for League of Legends in bulgarian Wikipedia
- Java fundamentals October 2015 - Memory Game - design the cards, backgrounds, functionality of the game
- Unity 3d February 2016 - Programmer's Dream - idea, design and functionality of the Menu Scene
- Software Technologies June 2016 - bYte me - short story - everything
- JavaScript Applications November 2016 - Tech Talks - functionality of the non-ReactJs version
- Databases Advanced February 2017 - Everyday Journal - idea, design and functionality of databases
- Practical Teamwork September 2017 - Chatche - Live chat - everything about UI Prototyping; List Processing - simple project to get to know the project tracking tools, which we decided to make it very object-oriented
- C# MVC Frameworks - ASP.NET Core - October 2017 - Meow - a cute, friendly web platform for cats and their future owners. In Meow you can adopt a kitty and such nice things.
Jumping Dude is C#-based Windows Forms game, with two heroes: The Dude and the enemy - Sonic.
The goal of the game is to reach the Diamond on the top of the screen by avoiding getting caught by
the enemy. Memory Game is a card-matching game with three themes and levels, which can be
chosen by the user. We used Java and Swing. In Programmer's Dream you can run endlessly and collect
1 and 0 through the Programmer's World. bYte me is a date site for programmers. Every user has profile
with likes and dislikes, and depending on the count of dislikes you can order rewards.
For this project I used C#, APS.NET MVC, Entity Framework, SQL Server, HTML and CSS.
Tech Talks is JavaScript and ReactJs-based blog, when users can add posts and read other's posts. And finally,
Everyday Journal is an online diary for people who are obsessed with healthy living. They can traverse
through days, write what they've consumed or their tasks for the day. In the Practical Teamwork course I had 3
different small task and 1 project with different teams. We worked with Git and source control systems, we
learned how to make a UI Prototyping for given web application and get to know what are these project tracking tools.


Home
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Blog Stuff
Reflect!
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Leaning Plan!
The Q's!
JS n DOM!
Neuroplasticity
Problem solving!
Learning plan!
The forgotten blog!


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Coordinate Systems, Colors and Symbols
In addition to the above there are state directives for color specification, coordinate system and symbol reference size/shape). There can be multiple instance of each overlay type and the state directives are "sticky". For instance, if you set the color, that color applies to all subsequent overlay directives on the command line until another color is specified. The coordinate system directive applies to all the overlays except "-grid", where there are specific arguments for this (which don't stick).
All the lines, symbols, and text are drawn / typeset anti-aliased and for labelled curves (e.g., latitude lines) the text follows the curve of the line. In a few situations some labels get twisted a little too fast and become hard to read but this is very rare.
Colors can be specified in standard HTML hex notation (e.g., "f1a3e7") (see, for instance, http://www.w3schools.com/html/html_colors.asp) or by a few names for convenience (black, white, red, green, blue, magenta, cyan, yellow, gray). A note on hex encoding: since people are so used to it from HTML, we allow a leading pound sign (e.g., "-color #a02ce8"). However, if put in scripts for shells based on sh the pound can be taken as the beginning of a comment block. So either avoid the pound, use a non-sh shell, or put the argument in quotes.
The coordinate system can be Equatorial, Galactic, or Ecliptic (in fact the code just matches the first two characters case insensitive so "ga" is adequate). Where appropriate, the system can be followed by epoch/equinox information (B/J for Besselian/Julian and a year). Of course this only applies to Equatorial and Ecliptic since Galactic does not precess.
The symbol directive sets the reference size for symbols and their shape. The size can be one of three variants: a relative size (based on a fraction of the plot height)[default], in arcseconds, arcminutes, or degrees (denoted by a 's', 'm', or 'd' suffix), or in pixels (a 'p' suffix).
Symbol drawing can be a little confusing. The text below describes the symbol syntax but it is often easier to absorb through examples. See: Montage Cookbook: Symbols
The symbol shape can be one of a pre-defined set: triangle, box/square, diamond, pentagon, hexagon, septagon, octagon, el (an right angle between two line segments), circle or compass. The el and compass symbols are usually used as stand-alone "compass rose" markers.
Alternatively, the shape can be a user-defined shape of which there are three types: a simple N-sided 'polygon', a 'starred' polygon (2N vertices with every other on a half radius), or a 'skeletal' polygon (N points connected to the center by radial lines). This mode requires two parameters in addition to the symbol size: the type and the value of N. Optionally, you can also give a rotation angle for any symbol. So "-symbol 2.0s polygon 3" is a triangle with one vertex pointing straight up and "-symbol 2.0s polygon 3 90." is the same triangle but with a vertex pointing to the right.
In the example on the right, we have three symbols, each with five "sides". The top one is a simple five-sided polygon, one arcminute in radius ("1m polygon 5"). The middle one is "starred" and 30 arcseconds in radius ("30s starred 5") and the bottome one is "skeletal" and fifteen arcseconds in radius ("15s skeletal 5").
Example state directives:
-csys Equatorial B1950 -csys ga -color red -color ff00ff -symbol 2.0 circle -symbol 15p starred 5 36.
Each of the overlay directives has additional parameters. Here are examples for the non-table overlays:
|
Coordinate Grid mViewer -color yellow \ -grid Equatorial J2000 \ -gray SDSS_g.fits 0s max gaussian-log \ -out grid.png Example data can be downloaded here. The coordinate system specification is the same as for the "-csys" directive above. |
|
|
Markers mViewer -color yellow \ -csys Equatorial J2000 \ -symbol 3.0 starred 5 36 \ -mark 202.41667 47.23333 \ -gray SDSS_g.fits 0s max gaussian-log \ -out marker.png The coordinate system and symbol already being set, all that is needed to place a marker is are current coordinates. |
|
|
Labels mViewer -color yellow \ -csys Equatorial J2000 \ -label 202.41667 47.23333 "Messier 031" \ -gray SDSS_g.fits 0s max gaussian-log \ -out label.png mViewer uses a general font mechanism but at this point only has one font loaded, so all the labelling needs to give are the coordinates and text string. About Fonts mViewer uses the FreeType library and the FreeSans.ttf font file. This includes regular Latin and Greek characters (as well as Japanese Kana, etc.) The FreeType library is comfortable with any TrueType font file, so swapping in an alternative one is straightforward.
The input to the library is Unicode, which many of todays applications (like editors) can handle directly. The details are application-specific, but if you can get special characters into the mViewer command (script files are a good way), mViewer will render them, e.g., -label 88.79294 7.40706 "α Orionis" |


| I l@ve RuBoard |
There are three kinds of sources of published information on Python: the standard Python documentation set, published books, and online material.
The standard Python documentation set includes five separate documents. They are all available in various formats (HTML, PDF, and PostScript, among others) at http://www.python.org/doc/. They are:
A fast-paced introduction to the language that most current Python programmers used to learn Python. It assumes a fair bit of previous programming knowledge, so novices tend to find it overwhelming in places, and it doesn't give Python's object-oriented features their due.
The most important of the Python books. It lists all the built-in functions and what the built-in type methods and semantics are, and describes almost all the modules that make up the standard distribution. It's well worth keeping on your local hard disk and consulting when in doubt about a specific function's interface or semantics, or when you can't remember specific method names for the built-in objects.
The most formal specification of the language itself. It gives the precise definition of syntactic operations, precedence rules, etc. Most users happily ignore it, but it does give the final word on intricate details of the language.
A document describing the precise rules of interaction between Python and C extensions (and the simpler case of embedding, when Python is being called by an existing C or C++ program). If you wish to write an extension module for Python, this book defines just what to do. The section on keeping track of references is especially important for tracking bugs in such modules.
A document describing the routines Python uses internally. You can also use these routines to manipulate Python objects from within C/C++ programs, usually in extension modules.
Like many other topics of interest on the Internet, Python has developed a list of Frequently Asked Questions. It's available at http://www.python.org/doc/FAQ.html, and covers everything from general information about Python (its name, origins, design choices, etc.) to issues arising when compiling or installing Python, programming questions, and more. The Python FAQ is maintained by the community at large. Any PSA member can log onto a web-driven program (a CGI program, like the one we saw in Chapter 10) and update existing entries or add new entries. As a result, the FAQ is both quite large and very current.
There are three books besides the one you're holding that are available in bookstores. These are:
Programming Python , by Mark Lutz, published by O'Reilly & Associates. This 860-page book is the logical next step after Learning Python. It covers in greater depth all the material covered here, and then some. Almost all aspects of Python are covered with progressively more sophisticated examples. Programming Python also discusses Python/C integration, and advanced applications such as Tkinter GUIs and persistence.
Internet Programming with Python, by Aaron Watters, Guido van Rossum, and James Ahlstrom, published by M&T Books. This is a 477-page book that provides an introduction to most of the Python, with special emphasis on writing programs to publish web pages.
The Python Pocket Reference, by Mark Lutz, published by O'Reilly & Associates. This is a short (75 pages) booklet listing the core aspect of the syntax, and the most commonly used modules and their function signatures. It covers Python 1.5.1.
The number of web pages describing Python modules, howto's, guides for novices, common tasks, etc., makes it impossible to list them all here. Instead, we'll encourage you to browse the Web, starting at the main Python web site. The PSA volunteers (Ken Manheimer, Andrew Kuchling, Barry Warsaw, and Guido van Rossum, to be precise) spend considerable effort making sure the web site is both comprehensive and well organized, so you shouldn't have a problem finding what you need. Most significant packages and modules have associated web pages and documentation for them.
| I l@ve RuBoard |


HI THERE! My name is Yi-Tsung Chen (Phillip), my background lies in the intersection of Business, Analytics and a little bit of UX Design across Taiwan, Singapore and Australia.
I am passion of using data to solve real-world problems, especially those are related to business operation and UX design aspects as can be seen from previous capstone and side projects shown below and or in my CV. Regarding personal interests, I like photography, traveling and learning. Nice to meet you and feel free to have a chat!
An A/B testing project examed the performance of MightyHive's Ads Retargeting Service.
Project LinkA supervised binary classification project applied several machine learning algorithms.
Project LinkApplied UX Quantitative and Qualitatuve Analysis tested the ease of functionality of a webapp startup - Qiamo
Project LinkScrapped Major League Baseball Data and peformed Exploratory Data Analysis and Visualization using Statcast Data.
Project Link


Helium NLTE¶
- The helium_treatment setting in the Tardis config. file will accept one of three options:
- none: The default setting. Populate helium in the same way as the other elements.
- recomb-nlte: Treats helium in NLTE using the analytical approximation outlined in an upcoming paper.
- numerical-nlte: To be implemented. Will allow the use of a separate module (not distributed with Tardis) to perform helium NLTE calculations numerically.
Recombination He NLTE¶
Paper version:
This section will summarise the equations used in the calculation of the helium state for the recomb-NLTE approximation in Tardis. A full physical justification for these equations will be provided in an upcoming paper. All of the level populations are given as a function of the He II ground state population (\(n_{2,1,0}\)), and the values are then normalised using the helium number density to give the correct level number densities.
- Symbols/Indexing:
- \(N_{i,j}\): Ion Number Density
- \(n_{i,j,k}\): Level Number Density
- i: atomic number
- j: ion number
- k: level number
(Note: An extra factor of \(\frac{1}{W}\) is included for the metastable states of He I.)
Code Version:
In the Tardis plasma, some of these equations are re-written slightly to make use of existing property methods (e.g. PhiSahaLTE, PhiSahaNebular) often using the relation:
Numerical He NLTE¶
Another helium_treatment option offered by Tardis is numerical-nlte. The use of this plasma property requires an additional code that is the property of Stephan Hachinger (see arXiv:1201.1506) and is not distributed with Tardis. Tardis also requires a specific atomic datafile to use this module. This plasma option is included so that people who have access to and permission to use the necessary module may use it. Otherwise, the recomb-NLTE option provides a very accurate alternative approximation.


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Value:
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Rectangles:
Start Time: , End Time:
Page no.
X
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Repository
See: Description
| Interface | Description |
|---|---|
| LinkClause |
| Class | Description |
|---|---|
| BackJoinFilter<T extends DataObject,BDO extends DataObject> |
Filter to select from a table based on the contents of
a table that points to it.
|
| ConstJoinerFilter<T extends DataObject,BDO extends DataObject> |
Filter to a specific row of a remote table.
|
| FieldOrderFilter<T> |
An OrderFilter that uses a single field
This is for modification loops that might change the default ordering and therefore
might fall foul of the chunking code.
|
| FilterAdd<T> |
Class to perform an in database increment based on a filter
|
| FilterDelete<T extends DataObject> |
Class to implement filter driven deletion.
|
| FilterPathParser<T extends DataObject> |
A class to parse a filter encoded as a path.
|
| FilterUpdate<T> |
Class to perform an in database update based on a filter
|
| FilterWipe<T> |
Class to null record fields based on a filter
|
| IdAcceptFilter<T> | |
| Joiner<T extends DataObject,BDO extends DataObject> |
Filter to join two tables, selecting entries based on a filter on the remote table
|
| JoinerFilter<T extends DataObject,BDO extends DataObject> |
Filter to join two tables.
|
| NullFieldFilter<T> |
filter to select on null/non-null fields
|
| OrphanFilter<T extends DataObject,BDO extends DataObject> |
Filter to select from a table where a local reference field fails to
resolve
|
| PrimaryOrderFilter<T> |
An
OrderFilter that enforces use of the PrimaryKey
This is for modification loops that might change the default ordering and therefore
might fall foul of the chunking code. |
| SelfReferenceFilter<T> |
Filter to select by IndexedReference
It selects on the primary key of the provided repository using a reference to the corresponding
data object.
|
| SQLFormFilter<T extends DataObject> |
Form based SQL only filter
|
| SQLIdFilter<T extends DataObject> |
Filter to select/exclude an entry by Id.
|
| SQLValueFilter<T> |
Simple SQLFilter to match based on the value of a field.
|
| WildCardReferenceFilter<T extends DataObject,P extends Indexed> |
Like
ReferenceFilter but also matches objects
where the reference field is null |
Repository


When a patient is ceased they will no longer be invited to be screened.
If you think this patient may need to be screened at a later date then you will need to either invite or defer the patient.


Photography is my first love, starting out at the age of 18 at the University of Alaska on an old Nikon FM-2 and a roll of black and white film. I worked towards a Fine Art Photography degree at UAA, earning myself a solo exhibition of my Advanced Photography course work.
I had no idea then that my passion for creating and capturing moments in time would develop into a career taking photos for hundreds of clients, from couples about to be married and Instagram models looking to break into the industry, to clothing brands looking for lifestyle shots and news outlets wanting coverage of massive music festivals.