WORDLIST2DAWG(1) WORDLIST2DAWG(1) NAME wordlist2dawg - convert a wordlist to a DAWG for Tesseract SYNOPSIS wordlist2dawg WORDLIST DAWG lang.unicharset wordlist2dawg -t WORDLIST DAWG lang.unicharset wordlist2dawg -r 1 WORDLIST DAWG lang.unicharset wordlist2dawg -r 2 WORDLIST DAWG lang.unicharset wordlist2dawg -l <short> <long> WORDLIST DAWG lang.unicharset DESCRIPTION wordlist2dawg(1) converts a wordlist to a Directed Acyclic Word Graph (DAWG) for use with Tesseract. A DAWG is a compressed, space and time efficient representation of a word list. OPTIONS -t Verify that a given dawg file is equivalent to a given wordlist. -r 1 Reverse a word if it contains an RTL character. -r 2 Reverse all words. -l <short> <long> Produce a file with several dawgs in it, one each for words of length <short>, <short+1>,... <long> ARGUMENTS WORDLIST A plain text file in UTF-8, one word per line. DAWG The output DAWG to write. lang.unicharset The unicharset of the language. This is the unicharset generated by mftraining(1). SEE ALSO tesseract(1), combine_tessdata(1), dawg2wordlist(1) http://code.google.com/p/tesseract-ocr/wiki/TrainingTesseract3 COPYING Copyright (C) 2006 Google, Inc. Licensed under the Apache License, Version 2.0 AUTHOR The Tesseract OCR engine was written by Ray Smith and his research groups at Hewlett Packard (1985-1995) and Google (2006-present). 02/09/2012 WORDLIST2DAWG(1)


--- layout: post status: publish published: true title: Welcome Back, Dusty author: display_name: Corey Fineran login: Corey email<EMAIL_ADDRESS>url: '' author_login: Corey author_email<EMAIL_ADDRESS>excerpt: |- Welcome back to Wrigley Field, Dusty. I hope to see some of those stellar management skills in the next three days. So you know, if you have any guys on the club that aren't necessarily the best players on the roster, but you like because they are "your guys", wordpress_id: 12 wordpress_url: http://ivyenvy.wordpress.com/?p=12 date: '2008-04-15 23:38:25 +0000' date_gmt: '2008-04-15 23:38:25 +0000' categories: - General Baseball tags: - Dusty Baker - Reds - Wrigley Field comments: [] ---
Welcome back to Wrigley Field, Dusty. I hope to see some of those stellar management skills in the next three days. So you know, if you have any guys on the club that aren't necessarily the best players on the roster, but you like because they are "your guys", feel free to start to mold the Reds into the club you would like for them to be.


class: center, middle # IEMS 5780
Building and Deploying Scalable
Machine Learning Services ### Lecture 2 - Machine Learning #### Albert Au Yeung
13th September, 2019 --- class: center, middle # Machine Learning --- # Agenda - Introduction to Machine Learning - Basic Concepts - Common Algorithms - Evaluation and Diagnosing - Machine Learning in Python --- class: middle, center # Introduction to Machine Learning --- class: equal-split # Machine Learning .column-left[ * A computer program generates an input given and output, given some **pre-defined function(s)** * ML aims at **learning** a function that maps the inputs to the outputs * Instead of having a programmer writing down the **logic**, we let the computer **learn** from the data * Given historical data, we train a **model** to generate predictions on **future or unseen** inputs ] .column-right[
] --- # Machine Learning * Given some input `\(X\)` and ouptut `\(y\)`, find a function `\(F(X)\)` that maps `\(X\)` to `\(y\)`. * Example: given (
location, size
) (`\(X\)`), predict the
price
of a house (`\(y\)`). * Another example: give (
previously watched movies
) (`\(X\)`), predict the
next movie(s) that will be watched
(`\(y\)`).
--- # Case Study: Spam Email Detection * Let's consider the task of detecting **[spam or junk emails](https://en.wikipedia.org/wiki/Email_spam)**. * Examples of spam emails: - scams - phishing - mass email marketing / advertisement - and [many more](http://web.mit.edu/network/spam/examples/) * How would you approach this problem? * (It is a **classification** task: for each email, we want to label it as "spam" or "non-spam") --- # Case Study: Spam Email Detection ## A non-ML approach * To classify incoming emails, we need to find some **patterns** that are unique in each classes * Example of useful patterns: - Name of the sender of the email - Key words/phrases in the subject (e.g. "You have won a lottery!") - A lot of words in capital letters in the content - ... * You then write a program to look for these patterns in a email received * **Done?** --- # Case Study: Spam Email Detection ## Limitation of a non-ML Approach * You may need to have **a lot of** rules * Rules too general may give you **false positives** * Rules too specific will be useless if the spammers make slight changes * How do you **weight** the importance of each rule? * ... --- # Case Study: Spam Email Detection ## A Machine Learning Approach
--- # Case Study: Spam Email Detection ## Advantages of using ML * Learn the important patterns **automatically** from the training data * An ML algorithm can go through **a large number of examples** to learn patterns * Able to discover the **relative importance** of different patterns * Model can be **re-trained** when the data has changed
*
Can you think of any weakness of ML?
--- # Basic Machine Learning Problems - **Supervised Learning**
You have **labelled data** for computer to learn from - Regression - Classification
- **Unsupervised Learning**
You don't have labelled data, but you want to find patterns in the data - Clustering / Dimensionality Reduction --- class: equal-split # Regression .column-left[ - In **regression**, we want to predict the value of a **continuous** variable based on some inputs - **Examples**: - Predict [the price of a house](https://www.kaggle.com/c/zillow-prize-1) - Predict [the number of products sold in a shop](https://www.kaggle.com/c/competitive-data-science-predict-future-sales) - Predict the number of users who will click an advertisement - Predict [the price of a product given its descriptions](https://www.kaggle.com/c/mercari-price-suggestion-challenge) ] .column-right[
(From Economist: [Corrosive corruption](https://www.economist.com/graphic-detail/2011/12/02/corrosive-corruption)) ] --- class: equal-split # Classification .column-left[ * Categorizing an input sample into one of the **pre-defined** classes * **Examples**: - Predict [whether a person will survive a shipwreck](https://www.kaggle.com/c/titanic) - Detect [the sentiment of a movie review](https://www.kaggle.com/c/movie-review-sentiment-analysis-kernels-only) - Predict [whether a driver will initiate an insurance claim](https://www.kaggle.com/c/porto-seguro-safe-driver-prediction) * In fact, a lot of problems can be defined as **classification problems** ] .column-right[
[ImageNet Classification with Deep Convolutional Neural Networks](www.cs.toronto.edu/~fritz/absps/imagenet.pdf) – Krizhevsky et al. 2012 ] --- class: equal-split # Unsupervised Learning / Clustering .column-left[ - To identify **hidden patterns** in a dataset without labels - **Examples**: - Understand what products are usually bought together ([Basket Analysis](https://en.wikipedia.org/wiki/Affinity_analysis)) - Understand major topics of news articles (e.g. [News Clustering](http://data-art.net/news_cluster/)) - Identifying [communities](https://en.wikipedia.org/wiki/Community_structure) in social networks - Clustering can be used to perform [dimensionality reduction](https://en.wikipedia.org/wiki/Dimensionality_reduction) ] .column-right[
Clusters of news articles about smog between May 2016 and May 2017 ([Source](https://www.weforum.org/agenda/2017/05/smog-in-5-charts/)) ] --- # Common ML Algorithms ### In the following, we will introduce a few common ML algorithms: * **Regression** - Linear regression
* **Classification** - Logistic regression - Decision trees
--- # Regression: Linear Regression * In regression, we want to predict the values of some targets given some **predictors** or **features** * In the simplest case, we can assume that the relationship between the features and the target is **linear**: $$y = a + bX$$ - In the equation above, `\(y\)` is the target, `\(X\)` is the feature, `\(a\)` is the intercept, and `\(b\)` is the weight of the feature - **Learning** means estimating a suitable value for both `\(a\)` and `\(b\)` such that the resultant straight line best approximate the data we observed --- class: equal-split # Linear Regression - Consider an example of predicting the **price of a house** given its **area** .column-left[ - A few samples from a house price dataset
- Each row can be considered as a **training sample** ] .column-right[
] --- class: equal-split # Linear Regression .column-left[ - Using the [ordinary least squares](https://en.wikipedia.org/wiki/Ordinary_least_squares) method, we can estimate the `\(a\)` and `\(b\)` in the equation - The line with the estimated values of `\(a\)` and `\(b\)` is plotted on the right - Note that **NOT** all points are lying on the line (Why?) - How can we **predict** the price of other houses? - How do we tell whether this is a **good** model? ] .column-right[
] --- # Linear Regression - In the above example, we have **one** feature, which is the area of the house - Can you think of **other features** that are useful for predicting the price? - If we have more features, the equation will become $$y = b_0 + b_1 x_1 + b_2 x_2 + ... + b_n x_n$$ - This is still a **linear regression** model, sometimes called **multiple linear regression** - `\(b_0\)` is called the **bias term**, while `\(b_1\)` to `\(b_n\)` are the weights of the features - `\(y\)` is a **weighted linear combination** of the values of `\(x_1\)` to `\(x_n\)` --- # Linear Regression * If we consider `\(x_0 = 1\)`, then we can write the linear regression equation in terms of vectors * Let `\(\boldsymbol{x}\)` be the feature vector `\((x_0, x_1, ..., x_n\))` representing the input * Let `\(\boldsymbol{b}\)` as a vector of the weights `\(b_0, b_1, ..., b_n\)`
(The `\(b_i\)`'s are the parameters of this model, and in many cases are denoted by `\(\boldsymbol{\theta}\)`) $$y = \theta^Tx$$ * If we consider the **whole training dataset** with `\(m\)` samples, and each input has `\(n\)` features (and thus we will have `\(n + 1\)` parameters):
--- # Summary ### What have we learnt? * What is a **model**? - Your assumptions of how things work - A simplification of the world (or at least the problem you are trying to solve) - A model is usually characterized by some **parameters** - (Thus machine learning usually involves **parameter optimization**) * The inputs are **represented** by **feature vectors** - **Representation** of input data is very important in ML - Very often we may even want to [learn how to represent the raw data](https://en.wikipedia.org/wiki/Feature_learning) --- # Classification * In classification, we are interested in putting each input sample into **two (or more) pre-defined classes** * In other words, the target variable `\(y\)` is **discrete** * Some **commong algorithms** for classification: - Logistic regression - Support vector machines - Decision Trees - K-nearest-neighbour (kNN) * Some regression tasks can be **simplified** to classification tasks - E.g. predicting whether the price of a stock will go up, down or unchanged instead of predicting its absolute value --- # Classification * For simplicity, we first consider the case in which we have **two classes** (binary classification) * You can give the classes any names (e.g. cat vs. dog, rainy vs. sunny), but when doing the maths, we would simply label them as `0` and `1` * Again, we consider that an input is represented as a **feature vector** * Can we directly apply linear regression to a classification problem? $$y = \theta^Tx$$ --- class: split # Classification .column-left[ * Consider an example of predicting whether a customer will **buy** a certain product given his/her demographic information * We represent each person as a **feature vector** * In our training data, we set `\(y = 1\)` if the customer has bought the product, and `\(y = 0\)` if the customer has not ] .column-right[
] --- # Logistic Regression * We can apply linear regression on this problem, but it is likely to perform badly * For classification, we want to have a **decision rule**, linear regression's output lies on a straight line, and may not allow picking a threshold easily * We don't really want the model to output some values too different from `0` or `1` * However, we can apply a **transformation** to the output of linear regression:
* `\(g(z)\)` is called the **logistic function** or **sigmoid function --- class: equal-split # Logistic Regression .column-left[
* The logistic function has certain characteristics - Its value is always bounded by 0 and 1 - Its value tends to `\(1\)` if `\(z\)` tends to `\(+ \infty\)`, and tends to `\(0\)` if `\(z\)` tends to `\(- \infty\)` - It is more likely to output values close to `\(0\)` or `\(1\)` ] .column-right[
] --- class: split # Decision Trees .column-left[ - Another commonly used algorithm for classification: **decision trees** - Consider the example on the right again - Can we generate some **rules** from the data that can allow us to predict the outcome accurately? - For example:
`(gender = M) AND (age < 40) --> buy = 1` - **Decision trees** are constructed by finding these conditions to **split** the dataset into smaller subsets ] .column-right[
] --- class: split # Decision Trees .column-left[ * A few algorithms have been proposed for **decision trees learning**, such as [ID3](https://en.wikipedia.org/wiki/ID3_algorithm) and [C4.5](https://en.wikipedia.org/wiki/C4.5_algorithm) * The general idea is similar: - Identify a feature which is **best** for spliting a given subset of the data - What is best is determined by some metrics, such as the [Gini coefficient](https://en.wikipedia.org/wiki/Gini_coefficient) or [information gain](https://en.wikipedia.org/wiki/Information_gain_in_decision_trees) - Create a **new node** using that feature - Continue the process on the subsets created by the split ] .column-right[
An example decision tree learned from the Titanic survival dataset.
] --- # Decision Trees * Decision trees can also be used to perform regression (thus the term **CART: Classification And Regression Trees**) * Decision trees are usually vulnerable to **overfitting** (more on this later), thus we usually have to control the **depth** of a tree * It is commonly used with **ensemble methods** to generate more accurate predictions, resulting in algorithms such as [random forests](https://en.wikipedia.org/wiki/Random_forest) or [gradient boosted trees](https://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting) * References: - [Decision Trees and Ensemble Models](https://ml.berkeley.edu/blog/2017/12/26/tutorial-5/) - [Decision Trees - scikit-learn](http://scikit-learn.org/stable/modules/tree.html) --- # Choosing ML Algorithms
http://scikit-learn.org/stable/_static/ml_map.png
--- class: middle, center # Basic Concepts in Machine Learning --- # Working on Machine Learning Projects * Nowadays, it is easy to apply machine learning to a problem and obtain some results ```python from sklearn.datasets import load_iris from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score iris = load_iris() X = iris.data # Training data inputs y = iris.target # Training data labels model = LogisticRegression() # Model initialization model.fit(X, y) # Training (fitting) y_pred = model.predict(X) # Generating predictions accuracy_score(y, y_pred) # Computing a metric (accuracy in this case) ``` * However, what would you do after this? Is it done *right*? Is it *good* enough? How to *improve* it? --- # Generalization * (**Generalization theory** is a huge topic in statistical machine learning, let's discuss it conceptually here) * When training a model, our hope is that what is learnt from **the past (training data)** can be applied to **predict the future (test data / unseen data)** * In general, we are making some assumptions: - the past and the future are **similar** - **patterns** observed in the past will appear in the future * Since our aim is to perform well in predicting the future: - Our model should **not only** achieve high accuracy on the training data - Our model needs to find out patterns that are likely to appear in **both the past and the future**, and ignore other **noise** --- # Model Complexity * A **model** can be considered as a simplified view of a problem - Some features that are important and are related to the target - The relationship between the features and the target (e.g. linear vs. non-linear) - Random noise * A **complex** model captures complex relationship between `\(X\)` and `\(y\)`, but it is also more likely to pick up noise --> **overfitting** * A **simple** model is easy to interpret, but may not be able to capture the true relationship between `\(X\)` and `\(y\)` --> **underfitting** --- class: middle, center # Evaluation --- # Evaluation * **Evaluation** is an important step in any machine learning project * Purposes: - Understand the **quality of predictions** of a trained model - Understand when does a model perform well or badly - Understand the **limitation** of the model * How: - Decide on a suitable **metric(s)** for your problem - Pick an algorithm and train a model - Compute the metric on the predictions on a test dataset --- # Evaluation ## Splitting Your Dataset * Let's assume that we have a dataset of 10,000 data points * You should not **train** your model and **evaluate** your model on the same dataset * **Why?** - We need to know whether the model can be generalized to **unseen** data
(The model may only be **memorizing** the training data) - We need some test dataset to help us **fine tune** our model(s) * **How?** - **Split** your dataset for different purposes --- # Splitting Your Dataset * It is usually advised that we have **three splits** of the dataset: 1. **training set**: for training your model(s) 2. **validation/development set**: for tuning your model's **hyperparameters** 3. **test/holdout set**: for testing the performance of your model * For example:
--- # Splitting Your Dataset * There is no standard of the ratio of train/val/test data samples * Generally: - You want to use **as much data as possible** to train your model - You want to have a **reasonable size** of validation and test data (so that your evaluation is meaningful) * When you really don't have enough data - It is also acceptable to split only into two sets: **train** and **test**
e.g. 70% for training and 30% for testing --- # Splitting Your Dataset * Splitting your data is NOT a trivial task * Normally, you **shuffle** your data and **randomly** splitting the data into different sets * Consider an **imbalanced dataset** for classification: 90% `0`s and 10% `1`s - If you split randomly, there are chances that you don't have any `1`s in your training dataset! - In this case, you may want to use [stratified sampling](https://en.wikipedia.org/wiki/Stratified_sampling), to make sure that the ratio of `0`s and `1`s in all subsets are 9:1.
--- # Splitting Your Dataset * Consider another task of predicting **time-series** data (forecasting) - Should you split your data **randomly**? * Your task is to train a model on **past** data to predict **future** targets * You should respect the **chronological order** of the data
--- # Cross Validation * The train/test procedure can actually be repeated on **different splits** * This usually reduces the **variance** of the estimated performance of a model * A commonly used cross validation approach is **K-fold cross validation** - For example, when **k=4**, data is split into **4** subsets: - Each time, **3** subsets are used for training, the remaining **1** is for testing
--- # Metrics * When evaluating the performance of a model, we need to have: - **ground truths**: the correct answer / the true labels of the inputs - **metric**: a measure of how good the predictions are compared to the ground truths * In the following, we focus on: - Metrics for **regression** tasks: usually a function that reflects how the **difference** between the predicted values and the true values - Metrics for **classification** tasks: usually a function that reflects how often the predicted values are the same as the true values * Ref: [Model evaluation: quantifying the quality of predictions](http://scikit-learn.org/stable/modules/model_evaluation.html) --- # Metrics for Regression * In regression tasks, for each test data point, we have a ground truth value `\(y\)` and a predicted value `\(\hat{y}\)` * Intuitively, a model is good if all `\(\hat{y}\)`'s are close to their respective `\(y\)`'s * Hence, a straight forward metric would be the **Mean Absolute Error (MAE)**: - The mean of the absolute differences for all samples $$ MSE(\boldsymbol{y}, \boldsymbol{\hat{y}}) = \frac{1}{n} \sum_{i=0}^{n-1}|y_i - \hat{y}_i| $$ --- # Metrics for Regression * Another commonly used metric for regression is the **Root Mean Squared Error (RMSE)** - The square root of the mean of squared differences for all samples - Let `\(\boldsymbol{y}\)` be the vector of ground truth, and `\(\boldsymbol{\hat{y}}\)` be the vector of predicted values, root mean squared error is defined by: $$ RMSE(\boldsymbol{y}, \boldsymbol{\hat{y}}) = \sqrt{\frac{1}{n} \sum_{i=0}^{n-1}(y_i - \hat{y}_i)^2} $$ --- # Metrics for Regression * MSE will be **larger** if the model makes predictions that are **far** from the truth values (due to the square)
https://stats.stackexchange.com/questions/147001/is-minimizing-squared-error-equivalent-to-minimizing-absolute-error-why-squared --- # Metrics for Classification * In classification, things are different: - The labels or classes do NOT lie on a continuous spectrum - Things are discrete: predictions are either **correct** or **incorrect** - We CANNOT measure **distance** between two labels * In the following, we will discuss some important metrics for classification: - Accuracy - True/False positives/negatives - Precision and Recall - Area Under the ROC Curve --- # Accuracy * A straight-forward metric for classification is **accruacy** * Defined by **number of correct predictions / total number of predictions** * For example: $$ y = (1, 0, 0, 0, 1, 1, 1, 0, 0) \\\ \hat{y} = (1, 1, 1, 0, 0, 1, 1, 0, 0) $$ * Then, accuracy is **6/9 = 66.7%** * This can also be applied to multi-class classification * What is the limitation of the **accuracy score**? --- # True/False Positives/Negatives * To have a better understanding of how well a model performs in predicting different classes * Consider a binary classification task, where we have classes **0** (negative) and **1** (positive) * We consider four different types of predictions: - **True Positives (TP)**: model predicted 1 correctly - **True Negatives (TN)**: model predicted 0 correctly - **False Positives (FP)**: it is 0, but model predicted 1 - **False Negatives (FN)**: it is 1, but model predicted 0 * By using the number of these four types of predictions, we can come up with some useful metrics --- # The Confusion Matrix
- Reference: [https://en.wikipedia.org/wiki/Confusion_matrix](https://en.wikipedia.org/wiki/Confusion_matrix) --- # Precision * **Precision** aims at answering the following question: - **How often the model is correct when it predicts the target should be a certain class?** * Precision is defined by: $$ Precision = \frac{TP}{TP + FP} $$ * For example: $$ y = (1, 0, 0, 0, 1, 1, 1, 0, 0) \\\ \hat{y} = (1, 1, 1, 0, 0, 1, 1, 0, 0) \\\ $$ * We have **TP = 3**, **FP = 2**, so **precision = 3/5 = 60%** --- # Recall * **Recall** aims at answering the following question: - **How many data of a certain class can the model correctly identifies?** * Recall is defined by: $$ Recall = \frac{TP}{TP + FN} $$ * For example: $$ y = (1, 0, 0, 0, 1, 1, 1, 0, 0) \\\ \hat{y} = (1, 1, 1, 0, 0, 1, 1, 0, 0) \\\ $$ * We have **TP = 3**, **FN = 1**, so **recall = 3/4 = 75%** --- class: equal-split # Task: Aeroplane Classifier .column-left[ * If a model outputs **0** (not-an-aeroplane) for all images: - accuracy = 7/9 = 77.7% - precision = (undefined) - recall = 0% * If a model outputs **1** (is-an-aeroplane) for all images: - accuracy = 2/9 = 22.2% - precision = 2/9 = 22.2% - recall = 2/2 = 100% ] .column-right[
] --- # Trade-off Between Precision and Recall * There is usually a **trade-off** between precision and recall * For a classifier that outputs a score between 0 and 1, setting the **threshold** has a significant impact on precision and recall * With a **high** threshold (e.g. 0.8) - Most predictions will be considered as **negatives** - Precision will tend to be **higher** (model is more **conservative**) - Recall will tend to be **lower** (likely to miss some positives) * With a **lower** threshold (e.g. 0.3) - Most predictions will be considered as **positives** - Precision will tend to be **lower** (more false positives) - Recall will tend to be **higher** (more positives identified) --- # F1 Score * **F1 Score** (or **F-Measure**) is a measure that takes [both precision and recall into account](https://en.wikipedia.org/wiki/F1_score) * In many cases, we would like to have a **balance** between precision and recall * F1 Score allows us to choose a model that is reasonable good in terms of both precision and recall * F1 is the **harmonic average** of precision and recall: $$ F_1 = \frac{2}{\frac{1}{Recall} + \frac{1}{Precision}} $$ --- # Ranking Positives and Negatives * In some tasks, we are interested in **accuracy**, ideally we want **every** prediction to be correct * However, in some other tasks, it is sufficient that we can identify **one (or a few) positive cases** from a batch of inputs (Examples?) * Or, in some cases, we are more concerned about the **ranking** of the inputs (Examples?) * Ideally we want the positives to be always scored **higher** than the negatives
--- # Area Under the ROC Curve * **AUC** allows us to estimate the ability of a model to rank positives higher than negatives * The **[ROC Curve](https://en.wikipedia.org/wiki/Receiver_operating_characteristic)** (Receiver Operating Characteristic Curve) is plot by varying the **decision threshold** to obtain different **true positive rates (TPR)** and **false positive rages (FPR)**: $$ TPR = Recall = \frac{TP}{TP + FN} $$ $$ FPR = \frac{FP}{FP + TN} $$ --- class: equal-split # Area Under the ROC Curve .column-left[ * Get a set of (FPR, TPR) points by varying the decision threshold * Plotting the points will usually result in a graph that looks like the one on the right * The larger the **area under the curve**, the better is your model (*Why?*) ] .column-right[
] --- class: middle, center # Machine Learning in Python --- # Machine Learning in Python * Many programming languages can be used to do machine learning, but mostly are done in **Python** * Python has recently found to be the most [popular programming language](https://www.economist.com/graphic-detail/2018/07/26/python-is-becoming-the-worlds-most-popular-coding-language) * A lot of machine learning algorithms and tools are available in Python
(Ref: [Top 20 Python Libraries for Data Science](https://medium.com/activewizards-machine-learning-company/top-20-python-libraries-for-data-science-in-2018-2ae7d1db8049)) * The [Jupyter notebook](http://jupyter.org/) has enabled interactive computing and fast prototyping with Python - Try it on [Google's Colaboratory](https://colab.research.google.com/) (You can even use GPU for free there!) --- # Machine Learning Libraries ### Some commonly used libraries/packages in Python for machine learning * **Numpy**, **Scipy** (numerical and scientific computation) * **Pandas** (data preprocessing and analysis) * **Scikit-learn** (machine learning) * **Gensim**, **NLTK** (natural language processing) * **Tensorflow**, **Keras**, **PyTorch** (deep learning) --- # Numpy and Scipy * These are packages for doing numerical and scientific computation * Numpy provides fast manipulation of **vectors** and **matrices** * Scipy provides various functions for **scientific computation** ```python import numpy as np a = np.random.rand(10) # a vector of length 10 randomly initialized b = np.random.rand(10) # another vector c = a.dot(b) # compute dot product of two vectors ``` --- # Scikit-learn * A widely used open source machine learning library implementing a log of commonly used machine learning algorithms * Most algorithms are well documented: (e.g. http://scikit-learn.org/stable/modules/tree.html) * It has a common API for most machine learning algorithms ```python from sklearn.linear_model import LinearRegression model = LinearRegression() # Create an instance of a model model.fit(X_train, y_train) # Fit a model (i.e. training) model.predict(X_new) # Apply model on new data points ``` --- # Scikit-learn ### Check the examples: * Linear regression:
[http://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html](http://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html) * Logistic regression:
[http://scikit-learn.org/stable/auto_examples/linear_model/plot_iris_logistic.html](http://scikit-learn.org/stable/auto_examples/linear_model/plot_iris_logistic.html) * Decision trees:
[http://scikit-learn.org/stable/modules/tree.html](http://scikit-learn.org/stable/modules/tree.html) --- class: center, middle # End of Lecture 2


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Heide Marathon
Stüde GER
Race Winners
(25 runnings)
23 Jun
2007
25
Jun
2006
26
Jun
2005
26
Jun
2004
28 Jun
2003 28 Jun 05
Jun
2002 04
Jun 13 Jun
2001
12 Jun
01 May
2000
30
Apr
29 Apr
02 May
01 May
1999
03
May
02
May
1998
01
May
1997 12 May
1996
11 May
09 Apr
1995
08
Apr
10 Apr 1994
25 Apr
1993
26
Apr 1992
27
Apr
1991
Notes: 1991 was the inaugural year. This event was held as a multi-day marathon
from 1994-2003. Only the first race counts for the number of runnings.
Winners for all races are shown, where known. In 2003, races were held in
the morning and in the afternoon. Years are given for the first running
for that year, i.e., to find the year, look up the list to the next date
showing a year.
2016
---
not held
---
not held
21 Jun
2015
3:11:36
Maik Jacobs (GER)
3:41:51
Alexa Rohrbach (GER)
29 Jun
2014
3:17:06
Steve Dolezych (GER)
3:47:10
Anke Meinberg (GER)
30
Jun 2013
3:06:04
Ingo Lampe (GER)
3:35:56
Andrea Jung (GER)
24 Jun
2012
2:56:03
Frank Balzer-2
4:00:04
Sarah Harrison (GER)
03 Jul
2011
2:51:39
Guido Deutsch-3
3:58:41
Haike Gries (GER)
13 Jun
2010
3:05:38
Frank Tschapka (GER)
3:52:49
Andrea Baschin (GER)
28 Jun
2009
3:00:00
Dirk Rolirad (GER)
3:31:17
Iris Brümmer-4
29 Jun
2008
2:52:03
Guido Deutsch-2
3:42:54
Iris Brümmer-3
3:03:24
Elger Bosle-2
3:43:49
Iris Brümmer-2
3:05:05
Elger Bosle (GER)
3:56:19
Iris Brümmer
(GER)
2:50:06
Frank Balzer (GER)
3:36:22
Erika Staebe (GER)
2:52:52
Guido Deutsch (GER)
3:19:18
Anja Scheele (GER)
3:02:07
Eduard Weinert (GER)
3:56:18
Angela Hoffman (GER)
3:25:34
Reme Wallesch (GER)
5:51:15
Heike Heide (GER)
2:57:13
Detlef Hirsbrunner (GER)
3:54:51
Petra Machovsky-2
2:56:41
Hans-Dieter Pannhorst (GER)
4:12:51
Patricia Zierke (GER)
3:07:49
Michael Berg (GER)
4:02:56
Petra Machovsky (GER)
3:14:59
Werner Selch (GER)
4:24:21
Barbara Szlachetka (GER)
3:12:41
Josef Schaefer-2
4:13:19
Helga Backhaus-3
2:53:53
Dirk Ideler (GER)
3:36:30
Helga
Backhaus-2
2:58:20
Josef Schaefer (GER)
4:07:31
Helga Backhaus (GER)
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Most Wins (1991-date)
Men
3
Guido Deutsch
(GER)
2004,2008,2011
Women
4
Iris Brümmer
(GER)
2006-09
Prize Money
No information on prize money has been received for this race.
Race Time Bias
Insufficient data to calculate the race time bias.
Competitive Level
(1990-date)
Insufficient data to calculate the competitive level.
Quality Performances
Men
None under 2:20
Women <2:40 1 <2:42 1 <2:44 1 <2:46 2 <2:48 2 <2:50 4
Last Updated on 30
Jul 2016
Contributors: Klaas Loonstra (36)


This code block protects against software weaknesses such as Missing/Incorrect Authorization
Resource separation/segmentation minimizes the impact of an unauthorized access by limiting the resources that a certain user or role has access to.
Resources can be network resources such as LANs or data resources such as different customer databases used in a SaaS offering.
If an attacker obtains access to the account of an administrator for one of the resources they do not have access to the other resources in the system.
Same approach can be applied to software development where URLs and other server resources can be segmented based on access.
This approach also avoids logical attacks. For example if an application defines a class of servlets that can only be accessed by administrators, this reduces the risk of logical flaws being introduced because the authorization logic can be simplified and declared in a base class.
"Divide the software into anonymous, normal, privileged, and administrative areas. Reduce the attack surface by carefully mapping roles with data and functionality. Use role-based access control (RBAC) [R.862.1] to enforce the roles at the appropriate boundaries. Note that this approach may not protect against horizontal authorization, i.e., it will not protect a user from attacking others with the same role."


Greetings!
Welcome to yet another JSonic example.
This page uses JSonicDeferreds to sync speech with other actions.
Let's review its code below.
We first import the JSonic module.
After the page finishes loading,
we create a JSonic instance.
Using dojo.query, we find all the paragraphs on the page
containing all of the text shown and spoken.
For each paragraph, we read the data-line attribute
which points to a line in the code to highlight.
We then queue up the paragraph text to speak.
The return value from say is a JSonicDeferred object.
We register a deferred callback to invoke just before this paragraph speech starts
and another to invoke just after the speech finishes.
In the before callback, we show the paragraph
and highlight its associated line of code.
In the after callback, we hide the paragraph
and cease highlighting any code lines.
All of this could be accomplished with a global observer callback
but deferreds make the code appear much more sequential.


Porsche Taycan CARB Certification Reveals Up To 282 Miles Of Range
We’re still waiting for official EPA numbers.
by Anthony AlanizAn eagle-eyed TaycanEVForum.com member posted a copy of the Porsche Taycan’s CARB (California AirResource Board) certificate that hints at the EV sedan’s real-world electric range. The document reveals the Taycan Turbo and Turbo S returning 281.8 and 282.7 miles, respectively.
The Porsche Taycan Is Making Rounds:
That’s more range than we estimated when Porsche debuted the new model. We expected the Porsches to return about 212 to 250 miles and 212 to 229 miles in the Turbo and Turbo S with the EPA test. Those figures were based on Porsche’s WLTP data, which gave the Turbo 237-280 miles of range and the Turbo S 241-256 miles. CARB’s numbers exceed both.
However, this isn’t the first time CARB’s number exceeded estimates. The board certified the Jaguar I-Pace with 334 miles of range, which far exceeds the 234-mile range given by the U.S. EPA. There’s a significant chance that when the EPA tests the Taycan Turbo and Turbo S, its numbers could be closer to our estimates or even lower, possibly hovering around 200 miles.
Gallery: 2020 Porsche Taycan
That’s not a lot, but the Taycan Turbo and Turbo S aren’t the company’s long-range EV offerings. These pack a performance punch worthy of the Turbo nameplate. The Turbo produces 616 horsepower (459 kilowatts), or up to 671 hp (500 kW) with Overboost. It rockets to 60 miles per hour (96 kilometers per hour) – with Launch Control – in 3.0 seconds. The Turbo S makes 616 hp and 751 hp (650 kW) with Overboost. A sprint to 60 mph happens in 2.6 seconds. Both have a 161-mph (259-kph) top speed.
Porsche’s EV lineup isn’t limited to the Taycan Turbo and Turbo S. In mid-October, a mere six weeks after debuting the Turbo siblings, Porsche revealed the Taycan 4S. Not only is this significantly cheaper than the Turbo – it starts at still-not-cheap $103,800. While Porsche didn’t release the 4S’s range, it should be better than the Turbo models. Maybe even better than their CARB rating, too.
Source: TaycanEVForum


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| PREV NEXT | FRAMES NO FRAMES |
| Packages that use com.hp.hpl.jena.graph.query | |
|---|---|
| com.hp.hpl.jena.db | A general database backend for persistent storage of Jena models. |
| com.hp.hpl.jena.graph | This package defines the Graph and Node family of classes, which form the underlying datatypes of the Jena system. |
| com.hp.hpl.jena.graph.compose | This package defines simple composite graphs - union, intersection, difference, and update-tracking. |
| com.hp.hpl.jena.graph.query | Defines the graph-level Query classes, allowing multi-pattern query over arbitrary graphs. |
| com.hp.hpl.jena.ontology | Provides a set of abstractions and convenience classes for accessing and manipluating ontologies represented in RDF. |
| com.hp.hpl.jena.rdf.model | A package for creating and manipulating RDF graphs. |
| com.hp.hpl.jena.util | Miscellaneous collection of utility classes. |
| Classes in com.hp.hpl.jena.graph.query used by com.hp.hpl.jena.db | |
|---|---|
| QueryHandler
a QueryHandler handles queries on behalf of a graph. |
| Classes in com.hp.hpl.jena.graph.query used by com.hp.hpl.jena.graph | |
|---|---|
| QueryHandler
a QueryHandler handles queries on behalf of a graph. |
| Classes in com.hp.hpl.jena.graph.query used by com.hp.hpl.jena.graph.compose | |
|---|---|
| QueryHandler
a QueryHandler handles queries on behalf of a graph. |
| Classes in com.hp.hpl.jena.graph.query used by com.hp.hpl.jena.graph.query | |
|---|---|
| Applyer
An Applyer object will run the StageElement next over all the
extensions of the Domain d which are derived from applying
the Matcher m to some internal supply of triples. |
|
| BindingQueryPlan
A BindingQueryPlan is something that can run executeBindings() to get back an ExtendedIterator over Domains, ie, Lists. |
|
| Domain
A Domain is an answer to a Binding query. |
|
| Dyadic
A base class for dyadic expressions with a built-in Valuator; subclasses must define an evalObject or evalBool method which will be supplied with the evaluated operands. |
|
| Element
An Element of a matching triple. |
|
| Expression
Expression - the interface for expressions that is expected by Query for constraints. |
|
| Expression.Application
An abstract base class for apply nodes; subclasses implement getFun(), argCount(), and getArg(). |
|
| Expression.Base
An abstract base class for Expressions; over-ride as appropriate. |
|
| Expression.Constant
An abstract base class for literal nodes; subclasses implement getValue(). |
|
| Expression.Valof
Valof provides an implementation of VariableValues which composes the "compile-time" VariableIndexes map with the "run-time" IndexValues map to produce a VariableValues map. |
|
| ExpressionSet
ExpressionSet: represent a set of (boolean) expressions ANDed together. |
|
| IndexValues
IndexValues: something that when supplied with a non-negative integer, returns a value. |
|
| Mapping
this class is used to record the mapping from [variable] Node's to the indexes they are bound to in a Query. |
|
| Matcher
A Matcher knows how to match itself against a concrete triple (the triple to be matched) and a domain (of bindings to check and extend). |
|
| NamedGraphMap
a mapping from from names to Graphs. |
|
| NamedTripleBunches
A NamedTripleBunches maps a [graph] name to a bunch of triples associated with that name. |
|
| Pattern
A Pattern represents a matching triple; it is composed of S, P, and O Elements. |
|
| PatternCompiler
A PatternCompiler is some class that knows how to handle fixed Node values, binding and bound occurences of variables, and wildcards. |
|
| PatternLiteral
PatternLiteral - an interface for pattern literals recognised by Rewrite. |
|
| PatternStageBase
PatternStageBase contains the features that are common to the traditional PatternStage engine and the Faster engine. |
|
| Pipe
A Pipe is anything that can be used to get and put Domain objects; it can be closed, and it can be tested for whether more elements are available. |
|
| Query
The class of graph queries, plus some machinery (which should move) for implementing them. |
|
| QueryHandler
a QueryHandler handles queries on behalf of a graph. |
|
| QueryNode
A QueryNode is a wrapped node that has been processed against some variable-binding map to discover (a) what kind of node it is and (b) what index in the binding map it has. |
|
| QueryNodeFactory
|
|
| QueryTriple
A QueryTriple is a composite of three QueryNodes derived from a single source triple. |
|
| SimpleQueryEngine.Cons
|
|
| Stage
a processing stage in the query pipeline. |
|
| StageElement
Class used internally by PatternStage to express the notion of "the runnable next component in this stage". |
|
| TreeQueryPlan
|
|
| TripleSorter
Interface for things that can sort triples (for optimising queries). |
|
| Valuator
Valuator - an interface for things that evaluate to some value, given a binding of variable (inexes) to values. |
|
| ValuatorSet
ValuatorSet - a set of Valuators, which can be added to and evaluated [only]. |
|
| VariableIndexes
VariableIndexes: a mapping from variable names to their offsets in some table of variable values. |
|
| VariableValues
The interface that allows the values of variables in expressions to be found. |
| Classes in com.hp.hpl.jena.graph.query used by com.hp.hpl.jena.ontology | |
|---|---|
| BindingQueryPlan
A BindingQueryPlan is something that can run executeBindings() to get back an ExtendedIterator over Domains, ie, Lists. |
| Classes in com.hp.hpl.jena.graph.query used by com.hp.hpl.jena.rdf.model | |
|---|---|
| QueryHandler
a QueryHandler handles queries on behalf of a graph. |
| Classes in com.hp.hpl.jena.graph.query used by com.hp.hpl.jena.util | |
|---|---|
| Query
The class of graph queries, plus some machinery (which should move) for implementing them. |
|
||||||||||
| PREV NEXT | FRAMES NO FRAMES |


POSSE (Professors’ Open Source Software Experience) is a workshop aimed at bringing world-wide professors to discuss how they can improve their CS courses in order to take advantage of free/open-source software (FOSS for short). FOSS is a topic very dear to my heart. When I was a student, I participated in several FOSS conferences and I also used to contribute to some FOSS projects. As a researcher (and, eventually, as a professor) I shifted my interest a bit. Although I’m not coding as much as I would like to, I’m still interested in FOSS. Indeed, some of my research research is about FOSS (e.g., communities, engagement, perceptions, etc). In addition, as of today, I’m a faculty running for tenure, with nearly no previous teaching experience. Needless to say that I was with high expectations for the workshop.
This year the workshop took place at the beautiful Bologna, Italy. This blog post describes my personal yet biased view about the 2 intensive days of chatting with other professors about CS courses, open-source, pasta, and wine. The workshop was a mix of several lectures and group activities. Although I agree that both parts are equally important, this blog post is focused on the lecture part, which I took some notes (I did not have time to write down notes during the group activities).
One of the first advice for professors that want to introduce FOSS in their courses is to inform students upfront that the course will not be a normal one. That is, the professor is not supposed to know everything about the FOSS under study (in fact, depending on the FOSS, no one knows), although, professors have to figure out with students what they do not know — which is perfectly fine. It is important to note that the learning curve is significant for both faculties and students. On the other hand, this approach provide excellent opportunities for technical/non-technical learning. We need to adapt the learning approach, though.
To minimize the learning curve, professors need materials. Good materials can be found at the teachingopensource.org and at the foss2serve.org websites. Take the time to explore them – they have a rich database.
While you learn something new from these websites, think about how students can contribute to open-source. One point that was largely debated is that open-source contributions is not only about code. There are many many ways that one can contribute – code is only one of them.  A FOSSer can (1) create a blog post, (2) test the software, or (3) answer questions in a malling list. There are, at least, 50 ways that one can contribute to FOSS. Find one that can fit your needs and tell students to do that.
However, we cannot throw a student that does not how to swim in the deep end of a pool and say: swim. We need a scaffold. Some questions you should ask yourself: What we need to do to contribute to FOSS? What are the steps? Forget students! What I have to do? After you figure out, think about how can you teach students to do that?
Since both professors and students are immersing into not well-known waters, it is important to have a contact within the FOSS community, who could provide help. How to find a contact? One way is to observe the community. Examples include: (1) Go to an IRC channel, (2) assign a malling list, or (3) participate in a slack group. After a while, you would be able to answer questions such as: Is this community active? Do they answer novice questions? Can you tell who are the core developers?
If this community seems to be a good one, then it is time to introduce yourself, explain that you have some students that might be able to contribute, describe your goal, etc. Is someone willing to help? Good! No? Do not give up. Repeat until you find a suitable community. If we have success finding good communities, the chances that students succeed should be higher.
Some students have a hard time trying to find meaningful things to contribute during the short term of a course. Some FOSS projects provide labels for “easy” tasks, while others provide Roadmaps (i.e., known features/bugs that the FOSS project want to have done/fixed for the next release). These are good ways to search for meaningful tasks. However, Roadmaps might contain critical bugs. Although the community do want to get them fixed (therefore they might provide good support for the student), such bugs might be non-trivial, requiring students to spend much time in learning (instead of coding) activities. It might be tempting to work on such fancy bugs. However, the suggestion is not to work on the critical path! This might delay the contribution process — and demotivate students.
Another thing to pay attention are the release dates. If there are releases scheduled during the course, contributions might be frozen for a while. Another challenge is related to architectural changes. If that is the case, some contributions might not be needed anymore (e.g., thanks for the fix, but this feature does not work like that anymore). The best way to prevent such problems is by understanding your community. Invest some time to figure out its needs and where it plans to go.
Remember that the goal is to engage students. Therefore, in some classes (e.g., high school, introductory classes), placing a source code contribution is not necessary. In some cases, understanding the steps for contributing might be ideal. In any case, some professors mentioned that the acceptance of a contribution should not be graded. This is out of the your (and students’) control. Instead, students can earn additional credits if the contribution is accepted.
It is important to change the mindset of having everything done before publishing. This not gonna work with FOSS. Teach students to release early and release often. This should be the norm. Also, tell students to avoid risks by working on small problems. One way to start is by working with documentation issues. Remember students that a lot of bugs are not code bugs. Typos are bugs! It is also important to engage students early – do not way for the last minute to provide a patch. How to do that? Create such assignment: get a patch by the end of this week.
After passing the first contribution barrier, prepare students to become independent in the FOSS world. Most of it is done by teaching students how to ask good questions. Rule of thumb: if Google has the answer, it is not a good question. I heard that it is better to have students that ‘‘know how to ask good questions, rather than students with great technical skills’’. The rationale here is that when you get stuck (and you will), you should know where to find help. Finding help, however, is not only about asking for help, but also about asking politely and asking at the right place (or for the right person). That is, communication skills are highly demanded. If you have students such as this one:
Just heard from a student: "I joined CS because I don't need to talk to anybody". What we did wrong?
— Gustavo Pinto (@gustavopinto) July 2, 2017
Remember them: Communication is part of your job!
All in all, I think this approach can lead to significantly improvements, in terms of both technical and non-technical skills. I will give it a try soon (and report the result too, hopefully).
If you have experience teaching CS with FOSS, why not reporting them? I’d love to read.


Pat Salmon Cookies was founded in 1998 by Patrick McDonald. He came with his family to Seattle from Ireland in 1997 and decided there was a need for this kind of cookies in Seattle.
Patrick, or Pat as everybody knows him, brought some special cookie cutters from the old country. To the left you can see a kind of cookie cutter he brought to the United States.
Click on the home link to check out latest events in the Seattle area.
Go to our store to order the latest type of new cookies on-line.
Click on the shopping kart link to see our latest promotions in t-shirts and a variety merchandise with the store's logo.
Main Store Location: 681 Crazy Rd
Lacey, WA 98503
Store Hours: 7 days a week 6:00am to 8:00am
Contact us via e-mal:
<EMAIL_ADDRESS>Author: Fortunato Maycotte


The Two and The Bubbles
By Vlad Gerasimov
Christmas Ice Skating
Love Knows No Boundaries
Sandal
Skiing
The Knight and The Lady
Friends
Coiffure
Get a Mac
Connection
Alice, Her Dragon, and The Christmas Tree
Inseparable
Pregnant
Christmas Tree
The Two and The Mouse
Punctuation
Internet Cafe
Raring Ringtail


Coinball
Strategy Types
1 - always rush
2 - always pass
3 - always rush when ahead or tied
4 - always pass when ahead or tied
Simulation
Player 1 Strategy:
Player 2 Strategy:
Number of Games to Simulate:
Player 1 Wins:
Player 2 Wins:


Bettor has $1K to win $1M on Warriors; Heat top NBA money team
by Todd DeweyA month into the 2019-20 NBA season, here’s a look at some of the league’s biggest early surprises and betting trends (through Thursday’s games).
— The Warriors were expected to regress this season after losing stars Kevin Durant to free agency and Klay Thompson to injury. But Golden State has completely collapsed after losing Steph Curry to a broken hand. The Warriors have the worst record in the NBA, at 3-13, after getting blown out by 48 points in Wednesday’s 142-94 loss to Dallas.
Golden State is 6-10 ATS and has seen its 2020 NBA title odds at the Westgate sportsbook soar to 1,000-1 after it was 12-1 in the preseason. One Westgate bettor placed a $1,000 wager to win $1 million on the Warriors to win it all. But Jeff Sherman, Westgate’s vice president of risk, isn’t too concerned.
“When or if Curry or Thompson returns, they’ll probably be 20 games out of a playoff spot,” Sherman said. “Right now, they’re 5½ games out of a playoff spot after 16 games. They’re trending in a downward direction and it doesn’t look like there will be any improvement.”
— The Clippers opened the season as plus 350 favorites to win the NBA title and the Lakers were the 4-1 second choice. The Los Angeles teams are now plus 250 co-favorites, followed by the Bucks (6-1), Rockets (8-1) and 76ers (9-1).
The Lakers have the league’s best record at 12-2 (9-5 ATS) while the Clippers improved to 10-5 (8-7 ATS) after beating Boston 107-104 in overtime Wednesday in their first game with Paul George and Kawhi Leonard in the lineup together.
“We had to lower the Lakers’ (odds) because they have such a soft schedule until December. They’re racking up all these wins and people are looking at their win-loss record and want to bet them based on that,” Sherman said. “I still think when the Clippers have Leonard and George healthy, they’re the the No. 1-rated team in the NBA.”
— The Celtics have started off surprisingly strong at 11-3 after losing Kyrie Irving (Nets) and Al Horford (76ers) in free agency. A Westgate bettor this week placed a $10,000 wager to win $200,000 on Boston (20-1) to win the NBA title.
— The Heat (10-3) have been the league’s top money team, going 10-2-1 ATS. Jimmy Butler leads Miami in scoring, averaging 18.7 points, but Miami has six other players averaging double figures, including rookies Tyler Herro (14.0 ppg) and Kendrick Nunn (17.3 ppg), who went undrafted out of Oakland.
“Kendrick Nunn has been a surprise on that team. He’s vaulted into the Rookie of the Year conversation. He’s (the) 6-1 (third choice) and wasn’t even listed in the preseason,” Sherman said. “He’s been contributing and Tyler Herro’s been contributing. They’ve been relying on young guys besides Butler.”
Ja Morant is the plus 175 favorite to win the Rookie of the Year Award and Zion Williamson is the 5-1 second choice. Herro is at 12-1.
— The Wizards have the second-best spread record, at 8-3-1, followed by the Celtics (8-4-2) and four teams that are 9-5 ATS: Lakers, Thunder, Raptors and Suns.
— The Spurs have the worst ATS mark at 3-12, followed by the Pistons and Magic at 4-9-1 and the Jazz and Grizzlies at 5-9.
— San Antonio leads the way with an 11-4 over-under mark, followed by the Mavericks (10-4), Wizards (7-4-1) and Trail Blazers (10-6).
— Utah is the best under team at 11-3, followed by the Nuggets (9-4) and Rockets, Clippers and Knicks at 10-5.
— Systemplays.com handicapper Doug Fitz shared a profitable NBA trend that calls for a play on the under whenever two teams with a winning percentage of at least 60 percent each play each other. The trend is 156-104-4 (60.0 percent) in December.
More Betting: Follow at reviewjournal.com/betting and @RJ_Sports on Twitter.
NBA betting
Through Thursday’s games
ATS records
Best
Heat; 10-2-1
Wizards; 8-3-1
Celtics; 8-4-2
Lakers; 9-5
Thunder; 9-5
Raptors; 9-5
Suns; 9-5
Worst
Spurs; 3-12
Pistons; 4-9-1
Magic; 4-9-1
Jazz; 5-9
Grizzlies; 5-9
Warriors; 6-10
Over-under records
Best over teams
Spurs; 11-4
Mavericks; 10-4
Wizards; 7-4-1
Trail Blazers; 10-6
Timberwolves; 9-6
Best under teams
Jazz; 11-3
Nuggets; 9-4
Rockets; 10-5
Clippers; 10-5
Knicks; 10-5


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CumanˆyagŸa,
Sierra
de
San
Juan
Alt.
3-400
m.
Collected
for
the
Atkins
Institution
of
the
Arnold
Arboretum
in
Santa
Clara
Province
CALIFORNIA
ACADEMY
OF
SCIENCES
(CAS)
Styrax
obtusifolius
Grisebach
Det.
by
Peter
W.
Fritsch,
April
2010
Styrax
obtusifolius
Griseb.
Examined
in
a
revision
of
the
North
American,
Central
American,
and
Caribbean
Styrax
(Styracaceae)
Linn.
Gene
J.
Gonsoulin
1970
Vanderbilt
University
01497494
Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high


{% extends "core/base.html" %} {% block site_title %}
Character name: {{character.name}}
Actual growth points: {{character.growth_points}}
Next level growth points: {{character.next_level}}
Start training date: {{training.start_training_date}}
Time to the end of the last training session: seconds!
Number of completed training sessions:
There is no character in the database
{% endif %} {% endblock %}


{% extends "basic.html" %} {% block head_title %}{{ APP_NAME }} your todolist{% endblock %} {% block body%} {% if user %}
you'll need to login to be able to add/edit/delete tasks.


{% extends base.html %} {% block header %}
An open, human-curated classification of neuroimaging literature.
PMID codes are 8 characters.
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torchtraining.device¶
Cast objects (usually torch.Tensor
) to specific device.
PyTorch’s defaults (as of 1.6.0
CPU and GPU) are forwarded for easier usage.
If you wish to use other (non-standard) devices (like TPUs), please use
Device
class explicitly, for example (TPU):
import torch_xla.core.xla_model as xm
class TrainStep(tt.steps.Train):
def forward(self, module, sample):
# Generate loss and other necessary items
...
return loss, predictions, targets
step = TrainStep(criterion, device)
# Select `loss` and perform backpropagation
step ** tt.Select(loss=0) ** tt.device.Device(xm.xla_device())
Note
IMPORTANT: Usually users should use torchtraining.device.CPU()
cast
after iteration
and before accumulation
in order not to pollute
GPUs memory.
-
class
torchtraining.device.
CPU
(memory_format=torch.preserve_format)[source]¶ Bases:
torchtraining._base.Operation
Cast
object
(usuallytorch.Tensor
) tocpu
.Note
IMPORTANT: This object should be used most often from this package in order to save
GPU
/TPU
memory. See example belowExample:
class TrainStep(tt.steps.Train): def forward(self, module, sample): ... return loss, accuracy step = TrainStep(criterion, device) iteration = tt.iterations.Train(step, module, dataloader) # Cast to CPU in order not to inflate GPU memory with `list` # You should usually use this OP before accumulation iteration ** tt.Select( accuracy=1 ) ** tt.device.CPU() ** tt.accumulators.List() ** tt.callbacks.Logger("Accuracy")
- Parameters
memory_format (torch.memory_format, optional) – The desired memory format of returned Tensor. Default: torch.preserve_format. Default:
torch.preserve_format
-
class
torchtraining.device.
CUDA
(device=None, non_blocking=False, memory_format=torch.preserve_format)[source]¶ Bases:
torchtraining._base.Operation
Cast
object
(usuallytorch.Tensor
) to cuda enabled device.Note
IMPORTANT: This object USUALLY SHOULDN’T BE USED as it usually pointlessly pollutes GPU memory.
- Parameters
device (torch.device | int, optional) – Device index to select. It’s a no-op if this argument is a negative integer or None. Default:
None
non_blocking (bool, optional) – If True and this copy is between CPU and GPU, the copy may occur asynchronously with respect to the host. For other cases, this argument has no effect. Default:
False
memory_format (torch.memory_format, optional) – The desired memory format of returned Tensor. Default: torch.preserve_format. Default:
torch.preserve_format
-
class
torchtraining.device.
Device
(device)[source]¶ Bases:
torchtraining._base.Operation
Cast
object
to any device (for exampleTPU
withtorch_xla
package).Note
IMPORTANT: This object USUALLY SHOULDN’T BE USED as it usually pointlessly pollutes device memory (unless it’s CPU, in such case simply use
torchtraining.device.CPU()
).See
example
at the beginning of this section.- Parameters
device (torch.device | Any) – Anything which can be used with
torch.Tensor.to
to cast onto specified device.


SMT and Model Checkers¶
The next steps up for tree searchers and SAT solvers:
- State more complex than just truth tables
- Searching a graph instead of a tree
- Temporal logic
Technical Lingo¶
- Satisfiability Modulo Theories (SMT) Problem
- Decision problem for logical first order formulas with respect to combinations of background theories such as: arithmetic, bit-vectors, arrays, and uninterpreted functions.
Dining Philosophers¶
Core model:
State of one chopstick ∈ { Unused, LeftPhilosopher, RightPhilsopher }
State of all chopsticks ∈ {'UUUUU', 'UUUUL', 'UUURL', 'RUURL', ... }
Number of possible states: 3⁵ = 243
Implied states for a philosopher:
0 chopsticks held ⟶ Thinking
1 chopsticks held ⟶ Trying to eat
2 chopsticks held ⟶ Eating
Unconstrained chopstick state transitions:
Unused ⟶ LeftPhilosopher
Unused ⟶ RightPhilsopher
LeftPhilosopher ⟶ Unused
RightPhilsopher ⟶ Unused
Constrain the model with philosopher strategies:
Strategy D:
0 chopsticks held ⟶ AcquireLeft
1 chopsticks held ⟶ AcquireRight
2 chopsticks held ⟶ ReleaseBoth
Strategy S:
Philosopher zero always holds left chopstick
Strategy H:
0 chopsticks held ∧ no request ⟶ Enqueue an eat request
0 chopsticks held ∧ requested but not available ⟶ Wait
0 chopsticks held ∧ requested available ⟶ AcquireLeft
1 chopsticks held ⟶ AcquireRight
2 chopsticks held ⟶ ReleaseBoth
What the Solver Does¶
The transitions generate a graph.
The solver traverses the graph to see if: 1) every state has an exit state – deadlocks 2) every path loop has every philosopher eating – no one starves
Unlike the puzzle solvers, we need “temporal operators” that check the relationship between a succession of states:
Predicate P is always true.
A chopstick is held by 0 or 1 philosophers
Predicate P is eventually true
A philosopher with a left chopstick eventually gets a right one
Predicate P is always eventually true
After eating, a philosopher is always able to eat again
For example:
UUUUU -> ... -> UURLU -> ULUUU -> ... -> UURLU -> ...
^ ^
| |
P₃ eats P₃ thinks P₃ eats
TLA⁺ model checker¶
The TLA⁺ model checker generate the contrained graph, follows all paths, and checks all of the temporal invariants.
Here’s PlusCal model for the dining philosophers problem.
For a nice write-up on using TLA⁺ for the dining philosophers problem see the blog post by Murat Demirbas at http://muratbuffalo.blogspot.com/2016/10/modeling-dining-philosophers-algorithm.html
Other Tools¶
The Z3Py package lets you drive Microsoft’s Z3 solver, a production ready powerful SMT solver.
To get started, see this tutorial: https://ericpony.github.io/z3py-tutorial/guide-examples.htm


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Abstract
The RESPOND project aims to deploy an interoperable energy automation, monitoring and control solution to deliver DR programs at a dwelling, building and district level.


{% extends "shop/base.html" %} {% load i18n %} {% block navbar %}
{% trans "Thank you for contacting us." %}
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cu(1) cu(1) NAME cu - Call up another system SYNOPSIS cu [ options ] [ system | phone | "dir" ] DESCRIPTION The cu command is used to call up another system and act as a dial in terminal. It can also do simple file transfers with no error checking. cu takes a single argument, besides the options. If the argument is the string "dir" cu will make a direct connection to the port. This may only be used by users with write access to the port, as it permits reprogram‐ ming the modem. Otherwise, if the argument begins with a digit, it is taken to be a phone number to call. Otherwise, it is taken to be the name of a system to call. The -z or --system option may be used to name a system beginning with a digit, and the -c or --phone option may be used to name a phone number that does not begin with a digit. cu locates a port to use in the UUCP configuration files. If a simple system name is given, it will select a port appropriate for that system. The -p, --port, -l, --line, -s and --speed options may be used to control the port selection. When a connection is made to the remote system, cu forks into two pro‐ cesses. One reads from the port and writes to the terminal, while the other reads from the terminal and writes to the port. cu provides several commands that may be used during the conversation. The commands all begin with an escape character, initially ~ (tilde). The escape character is only recognized at the beginning of a line. To send an escape character to the remote system at the start of a line, it must be entered twice. All commands are either a single character or a word beginning with % (percent sign). cu recognizes the following commands: ~. Terminate the conversation. ~! command Run command in a shell. If command is empty, starts up a shell. ~$ command Run command, sending the standard output to the remote system. ~| command Run command, taking the standard input from the remote system. ~+ command Run command, taking the standard input from the remote system and sending the standard output to the remote system. ~#, ~%break Send a break signal, if possible. ~c directory, ~%cd directory Change the local directory. ~> file Send a file to the remote system. This just dumps the file over the communication line. It is assumed that the remote system is expect‐ ing it. ~< Receive a file from the remote system. This prompts for the local file name and for the remote command to execute to begin the file transfer. It continues accepting data until the contents of the eofread variable are seen. ~p from to, ~%put from to Send a file to a remote Unix system. This runs the appropriate com‐ mands on the remote system. ~t from to, ~%take from to Retrieve a file from a remote Unix system. This runs the appropri‐ ate commands on the remote system. ~s variable value Set a cu variable to the given value. If value is not given, the variable is set to true. ~! variable Set a cu variable to false. ~z Suspend the cu session. This is only supported on some systems. On systems for which ^Z may be used to suspend a job, ~^Z will also suspend the session. ~%nostop Turn off XON/XOFF handling. ~%stop Turn on XON/XOFF handling. ~v List all the variables and their values. ~? List all commands. cu also supports several variables. They may be listed with the ~v command, and set with the ~s or ~! commands. escape The escape character. Initially ~ (tilde). delay If this variable is true, cu will delay for a second after recogniz‐ ing the escape character before printing the name of the local sys‐ tem. The default is true. eol The list of characters which are considered to finish a line. The escape character is only recognized after one of these is seen. The default is carriage return, ^U, ^C, ^O, ^D, ^S, ^Q, ^R. binary Whether to transfer binary data when sending a file. If this is false, then newlines in the file being sent are converted to car‐ riage returns. The default is false. binary-prefix A string used before sending a binary character in a file transfer, if the binary variable is true. The default is ^V. echo-check Whether to check file transfers by examining what the remote system echoes back. This probably doesn't work very well. The default is false. echonl The character to look for after sending each line in a file. The default is carriage return. timeout The timeout to use, in seconds, when looking for a character, either when doing echo checking or when looking for the echonl character. The default is 30. kill The character to use delete a line if the echo check fails. The default is ^U. resend The number of times to resend a line if the echo check continues to fail. The default is 10. eofwrite The string to write after sending a file with the ~> command. The default is ^D. eofread The string to look for when receiving a file with the ~< command. The default is $, which is intended to be a typical shell prompt. verbose Whether to print accumulated information during a file transfer. The default is true. OPTIONS The following options may be given to cu. -e, --parity=even Use even parity. -o, --parity=odd Use odd parity. --parity=none Use no parity. No parity is also used if both -e and -o are given. -h, --halfduplex Echo characters locally (half-duplex mode). --nostop Turn off XON/XOFF handling (it is on by default). -E char, --escape char Set the escape character. Initially ~ (tilde). To eliminate the escape character, use -E ''. -z system, --system system The system to call. -c phone-number, --phone phone-number The phone number to call. -p port, --port port Name the port to use. -a port Equivalent to --port port. -l line, --line line Name the line to use by giving a device name. This may be used to dial out on ports that are not listed in the UUCP configuration files. Write access to the device is required. -s speed, --speed speed The speed (baud rate) to use. -# Where # is a number, equivalent to --speed #. -n, --prompt Prompt for the phone number to use. -d Enter debugging mode. Equivalent to --debug all. -x type, --debug type Turn on particular debugging types. The following types are recog‐ nized: abnormal, chat, handshake, uucp-proto, proto, port, config, spooldir, execute, incoming, outgoing. Only abnormal, chat, hand‐ shake, port, config, incoming and outgoing are meaningful for cu. Multiple types may be given, separated by commas, and the --debug option may appear multiple times. A number may also be given, which will turn on that many types from the foregoing list; for example, --debug 2 is equivalent to --debug abnormal,chat. --debug all may be used to turn on all debugging options. -I file, --config file Set configuration file to use. This option may not be available, depending upon how cu was compiled. -v, --version Report version information and exit. --help Print a help message and exit. BUGS This program does not work very well. AUTHOR Ian Lance Taylor<EMAIL_ADDRESS>Taylor UUCP 1.07 cu(1)


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cp file1 file2 is the command which makes a copy of file1 in the current working directory and calls it file2
What we are going to do now, is to take a file stored in an open access area of the file system, and use the cp command to copy it to your unixstuff directory.
First, cd to your unixstuff directory.
% cd ~/unixstuff
Then at the UNIX prompt, type,
% cp /vol/examples/tutorial/science.txt .
(Note: Don't forget the dot (.) at the end. Remember, in UNIX, the dot means the current directory.)
The above command means copy the file science.txt to the current directory, keeping the name the same.
(Note: The directory /vol/examples/tutorial/ is an area to which everyone in the department has read and copy access. If you are from outside the University, you can grab a copy of the file here. Use 'File/Save As..' from the menu bar to save it into your unixstuff directory.)
Create a backup of your science.txt file by copying it to a file called science.bak
mv file1 file2 moves (or renames) file1 to file2
To move a file from one place to another, use the mv command. This has the effect of moving rather than copying the file, so you end up with only one file rather than two.
It can also be used to rename a file, by moving the file to the same directory, but giving it a different name.
We are now going to move the file science.bak to your backup directory.
First, change directories to your unixstuff directory (can you remember how?). Then, inside the unixstuff directory, type
% mv science.bak backups/.
Type ls
and ls backups
to see if it has worked.
To delete (remove) a file, use the rm command. As an example, we are going to create a copy of the science.txt file then delete it.
Inside your unixstuff directory, type
% cp science.txt tempfile.txt
% ls (to check if it has created the file)
% rm tempfile.txt
% ls (to check if it has deleted the file)
You can use the rmdir command to remove a directory (make sure it is empty first). Try to remove the backups directory. You will not be able to since UNIX will not let you remove a non-empty directory.
Create a directory called tempstuff using mkdir , then remove it using the rmdir command.
Before you start the next section, you may like to clear the terminal window of the previous commands so the output of the following commands can be clearly understood.
At the prompt, type
% clear
This will clear all text and leave you with the % prompt at the top of the window.
The command cat can be used to display the contents of a file on the screen. Type:
% cat science.txt
As you can see, the file is longer than than the size of the window, so it scrolls past making it unreadable.
The command less writes the contents of a file onto the screen a page at a time. Type
% less science.txt
Press the [space-bar]
if you want to see another page, type [q]
if you want to quit reading. As you can see, less is used in preference
to cat for long files.
The head command writes the first ten lines of a file to the screen.
First clear the screen then type
% head science.txt
Then type
% head -5 science.txt
What difference did the -5 do to the head command?
The tail command writes the last ten lines of a file to the screen.
Clear the screen and type
% tail science.txt
How can you view the last 15 lines of the file?
Using less, you can search though a text file for a keyword (pattern). For example, to search through science.txt for the word 'science', type
% less science.txt
then, still in less (i.e. don't press [q] to quit), type a forward
slash [/]
followed by the word to search
/science
As you can see, less finds and highlights the keyword. Type [n]
to search for the next occurrence of the word.
grep is one of many standard UNIX utilities. It searches files for specified words or patterns. First clear the screen, then type
% grep science science.txt
As you can see, grep has printed out each line containg the word science.
Or has it????
Try typing
% grep Science science.txt
The grep command is case sensitive; it distinguishes between Science and science.
To ignore upper/lower case distinctions, use the -i option, i.e. type
% grep -i science science.txt
To search for a phrase or pattern, you must enclose it in single quotes (the apostrophe symbol). For example to search for spinning top, type
% grep -i 'spinning top' science.txt
Some of the other options of grep are:
-v display those lines that do NOT match
-n precede each maching line with the line number
-c print only the total count of matched lines
Try some of them and see the different results. Don't forget, you can use more than one option at a time, for example, the number of lines without the words science or Science is
% grep -ivc science science.txt
A handy little utility is the wc command, short for word count. To do a word count on science.txt, type
% wc -w science.txt
To find out how many lines the file has, type
% wc -l science.txt
cp file1 file2 |
copy file1 and call it file2 |
mv file1 file2 |
move or rename file1 to file2 |
rm file |
remove a file |
rmdir directory |
remove a directory |
cat file |
display a file |
more file |
display a file a page at a time |
head file |
display the first few lines of a file |
tail file |
display the last few lines of a file |
grep 'keyword' file |
search a file for keywords |
wc file |
count number of lines/words/characters in file |
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The main page is a summary of the user's League of legend profile and Lol Mates profile.
The search page allows the connected user to seek players matching some criteria like : Current league; Country; Preferred position (Top, Jungle ...); Voice communication instrument (Skype, Discord...)..
The stats page gives statistics on the 3 more played champions during the 10 last games for the selected player.


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--- layout: book title: "Service Mesh Patterns" date: 2020-04-08 21:15:05 +0000 image: /assets/images/oreilly-logo.png #author: Lee Calcote status: upcoming brief: "Patterns, recipes and best practices for deploying and operating service meshes." ---
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جدول المحاضرات
| 9 - 11 |
11 - 12 |
12 - 3 |
|
| الســبت |
معالجات | معمل معالجات | |
| الأحـــــد |
قواعد بيانات | معمل قواعد بيانات | |
| الاثنــــين |
تصميم شبكات | نظم تشغيل | معمل تصميم شبكات |
| الثلاثــــاء |
برمجة | تقارير فنية | معمل برمجة |
| الأربعــــاء |
|||
| الخمــــيس |
صيانة | معمل صيانة |
| 9 - 11 |
11 - 12 |
12 - 3 |
|
| الســبت |
معالجات | معمل معالجات | |
| الأحـــــد |
قواعد بيانات | معمل قواعد بيانات | |
| الاثنــــين |
تصميم شبكات | نظم تشغيل | معمل تصميم شبكات |
| الثلاثــــاء |
برمجة | تقارير فنية | معمل برمجة |
| الأربعــــاء |
|||
| الخمــــيس |
صيانة | معمل صيانة |
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.
If you use the .sr-only class to hide a form control's (rather than using other labelling options, such as the aria-label attribute), Bootstrap will automatically adjust the position of the icon once it's been added.
If you use the .sr-only class to hide a form control's (rather than using other labelling options, such as the aria-label attribute), Bootstrap will automatically adjust the position of the icon once it's been added.


| Number | Description | Something Else |
|---|---|---|
| 1 | A job | With details |
| 2 | Another one | More stuff |
| 3 | Lorem Ipsum | Latin |
| 4 | Lorem Ipsum | Latin |
| 5 | Lorem Ipsum | Latin |
| Number | Description | Something Else |
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| 2 | Another one | More stuff |
| 3 | Lorem Ipsum | Latin |
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| 5 | Lorem Ipsum | Latin |


Describes the hour, minute and second values used to set the media clock.
@since SDL 1.0
Create a time struct with a time interval (time in seconds). Fractions of the second will be eliminated and rounded down.
- (nonnull instancetype)initWithTimeInterval:(NSTimeInterval)timeInterval;
init(timeInterval: TimeInterval)
The time interval to transform into hours, minutes, seconds
The object
Create a time struct with hours, minutes, and seconds
- (nonnull instancetype)initWithHours:(UInt8)hours
minutes:(UInt8)minutes
seconds:(UInt8)seconds;
init(hours: UInt8, minutes: UInt8, seconds: UInt8)
The number of hours
The number of minutes
The number of seconds
The object
The hour of the media clock
Some display types only support a max value of 19. If out of range, it will be rejected.
Required, Integer, 0 - 59
@property (nonatomic, strong) NSNumber<SDLInt> *_Nonnull hours;
var hours: NSNumber & SDLInt { get set }
The minute of the media clock
Required, Integer, 0 - 59
@property (nonatomic, strong) NSNumber<SDLInt> *_Nonnull minutes;
var minutes: NSNumber & SDLInt { get set }
The second of the media clock
Required, Integer, 0 - 59
@property (nonatomic, strong) NSNumber<SDLInt> *_Nonnull seconds;
var seconds: NSNumber & SDLInt { get set }


Experiences targeted to stay-at-home parents looking for something new to do! Join our community to meet new people and create wonderful experiences for you and your children. From eating out to visiting a national park. Looking for something new? Create your own!


This project by Lukas Vermeer is hosted on Github. For more details about the underlying concepts, and recommendations for experiment design calculations, please refer to Beyond Power Calculations: Assessing Type S (Sign) and Type M (Magnitude) Errors by Andrew Gelman and John Carlin.


Gecode 72 crossword instances
These are to be used by
/picat/crossword_gecode_problems.pi
.
gecode_grid0.txt
gecode_grid1.txt
gecode_grid2.txt
gecode_grid3.txt
gecode_grid4.txt
gecode_grid5.txt
gecode_grid6.txt
gecode_grid8.txt
gecode_grid9.txt
gecode_grid7.txt
gecode_grid10.txt
gecode_grid11.txt
gecode_grid12.txt
gecode_grid13.txt
gecode_grid14.txt
gecode_grid15.txt
gecode_grid16.txt
gecode_grid17.txt
gecode_grid18.txt
gecode_grid19.txt
gecode_grid20.txt
gecode_grid21.txt
gecode_grid22.txt
gecode_grid23.txt
gecode_grid24.txt
gecode_grid25.txt
gecode_grid26.txt
gecode_grid27.txt
gecode_grid28.txt
gecode_grid29.txt
gecode_grid30.txt
gecode_grid31.txt
gecode_grid32.txt
gecode_grid33.txt
gecode_grid34.txt
gecode_grid35.txt
gecode_grid36.txt
gecode_grid37.txt
gecode_grid38.txt
gecode_grid39.txt
gecode_grid40.txt
gecode_grid41.txt
gecode_grid42.txt
gecode_grid43.txt
gecode_grid44.txt
gecode_grid45.txt
gecode_grid46.txt
gecode_grid47.txt
gecode_grid48.txt
gecode_grid49.txt
gecode_grid50.txt
gecode_grid51.txt
gecode_grid52.txt
gecode_grid53.txt
gecode_grid54.txt
gecode_grid55.txt
gecode_grid56.txt
gecode_grid57.txt
gecode_grid58.txt
gecode_grid59.txt
gecode_grid60.txt
gecode_grid61.txt
gecode_grid62.txt
gecode_grid63.txt
gecode_grid64.txt
gecode_grid65.txt
gecode_grid66.txt
gecode_grid67.txt
gecode_grid68.txt
gecode_grid69.txt
gecode_grid70.txt
gecode_grid71.txt


{namespace vh=In2code\Powermail\ViewHelpers}
Show Confirmation Page
{powermail_rte} Variable is filled with values from RTE in backend (from thx page)
{powermail_all} Outputs all fields
{marker1}, {firstname}, etc.. Outputs a field
{mail} Complete Mail Object
{ttContentData} All values from content element with plugin
{uploadService} All values from uploaded files


Tangente, the iconic NOMOS timepiece, is celebrating its updated date from all sides—and interpreting it in an entirely new way: The date ring, placed far out around the edge of the dial, features two red dots that frame the current date. An aesthetic that can be attributed to the new neomatik caliber within. It's unique, and unmistakably NOMOS Glashütte.


Critics say Stephanie Baird’s AFTER is...
“A masterpiece of the modern horror genre.” - Jacob Buchanan, The Daily Times.
“Sure to be considered a classic in the field. I couldn’t put it down - or sleep!” - Monique Lester, Modern Writer Magazine.
“Truly amazing. A tour de force from an author destined to be considered one of the greats in her chosen literary genre.” - Quinn Janson, SF, Fantasy, and Horror Review.
Buy AFTER Online At: Amazon| Barnes and Noble | iTunes | Kobo | Smashwords
Get the first chapter of AFTER free when you sign up for our mailing list!
You’ll also be the first to know when Stephanie has a new book coming out, and we never spam!
Recent News
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Find Stephanie online at:


tests.operation module
Tests for the file_helper.operation module
-
class
tests.operation.
HashFileTestCase
(methodName='runTest')[source]
Bases: tests.BaseTest.BaseTest
operation.hash_file() test cases
-
test_sample_md5
()[source]
Test the sample file md5
-
test_sample_sha1
()[source]
Test the sample file sha1
-
test_sample_slice
()[source]
Test the slice of a sample file
-
class
tests.operation.
ReadFileTestCase
(methodName='runTest')[source]
Bases: tests.BaseTest.BaseTest
operation.read_file() test cases
-
test_sample_1k
()[source]
Test the sample file
-
test_sample_slice
()[source]
Test the sample file
-
class
tests.operation.
SliceFileTestCase
(methodName='runTest')[source]
Bases: tests.BaseTest.BaseTest
operation.slice_file() test cases
-
test_sample_2k
()[source]
Test the sample file
-
test_sample_slice
()[source]
Test the sample contents
-
class
tests.operation.
WriteFileTestCase
(methodName='runTest')[source]
Bases: tests.BaseTest.BaseTest
operation.write_file() test cases
-
test_append
()[source]
Test the append of data to a sample file
-
test_new_1k
()[source]
Test if a write creates the same file as sample
-
test_overwrite
()[source]
Test the overwrite of a sample


jp.kobe_u.copris
Class for encoding CSP to PB
Class for PB DSL
Class for PB solvers
Class for analyzing PB solver log output
Class for Sat4j solver
Class for solvers
Class for translating CSP to PB
Object for clasp solver ("clasp")
Object for Sat4j solver
Object for PB DSL


See patients list and their prescriptions what they have added. You have to ask for approval to view them
Find out requests that you have made and their status. You may view them if patient has approved it


--- title: Overview ---
FFWD is a flexible metric forwarding agent. It is intended to run locally on the system and receive metrics through a wide set of protocols and then forward them to your TSDB. By running locally, it is easily available to receive pushed data from any application or service that is running on the same system. FFWD decorates the received metrics with system-wide tags or attributes. By doing this, the application generating the data becomes simpler to build, maintain, and configure since it doesn't have to know where it is running. Only that ffwd is available on the loopback interface.


Sign up in the Kibitz homepage and create a recommender. Then, download the supporting files from the Kibitz Github Repo before using the following API. To see example applications in a variety of different languages, see our examples on Github. You must import the following Javascript files:
<script type="text/javascript" src="kibitz/js/thrift.js"></script> <script type="text/javascript" src="kibitz/js/kibitz_types.js"></script> <script type="text/javascript" src="kibitz/js/RecommenderService.js"></script> <script type="text/javascript" src="kibitz/js/required_functions.js"></script>In the
required_functions.js
file, please input the following information:
var transport = new Thrift.Transport("http://kibitz.csail.mit.edu:9889/kibitz/"); var protocol = new Thrift.Protocol(transport); var client = new kibitz.RecommenderServiceClient(protocol); transport.open(); var client_key = YOUR_RECOMMENDER_CLIENT_KEY; var datahub_username = YOUR_DATAHUB_USERNAME;Now you are ready to begin customizing your recommender.
You must include the following function before the you call any other Kibitz methods. The following snippet is presented in Javascript. To see other languages, view our example recommenders.
client.initiateModel(YOUR_RECOMMENDER_CLIENT_KEY, YOUR_DATAHUB_TABLE_NAME, YOUR_DATAHUB_USERNAME, YOUR_DATAHUB_REPOSITORY_NAME);
After you call this function, you may call any of the following functions. For example, if you want to get a list of recommended items based on user ratings, you may provide the user ID of the user and number of recommendations you want in the following API call:
client.makeRecommendation(CLIENT_KEY, USER_ID, NUM_RECS, False, DISPLAY_COLUMNS);
The Item
data struct is used to return information about an item. Each Item
contains the following information:
struct Item { map<string, string> attributes int item_id }
The Item
object contains the item_id
of the item and a hash table of display column names to the values contained in each column for the item. The item_id
is
the primary key of the item.
The API functions given below give you greater flexibility when creating your recommendation service.
list<Item>makeRecommendation(CLIENT_KEY, userId, numRecs, False, displayColumns)
: Takes your client key, the user id of the user you want to make recommendations for,
the number of recommendations you want to make, and a list of strings of columns you want to display on your website. Outputs a list of Item
objects as recommended items. If you provided a ranking column,
the recommendations will also include the highest ranked items.
list<Item> getPageItems(CLIENT_KEY, long page, long numPerPage, list<string> displayColumns)
: Takes client key, the page number, the number of items per page, and the names
of the columns that you want to display. Returns a list of Item
objects to be displayed.
long getItemCount(CLIENT_KEY)
: Takes the client key and returns the total number of items in your items table.
void recordRatings(CLIENT_KEY, long userId, long itemId, long rating)
: Takes the client key, the user id of the user you want to record ratings for, the item id (primary key) of the item that
the user rated, and the rating and records it into the corresponding ratings table in Datahub.
void deleteRatings(CLIENT_KEY, long userId, long itemId)
: Takes client key, the user id of the user you want to delete ratings from, and the item id (primary key) that you want
to delete the rating for.
string createNewUser(CLIENT_KEY, string username, False)
: Takes the client key, the desired username of the new user to your website, and False
.
Returns whether the username already exists. We recommend that you use the Datahub API for logging in to track user activity across multiple recommenders.
long retrieveUserId(CLIENT_KEY, string username)
: Takes the client key and the username of the user you want to retrieve the id of.
Returns the id of the user or -1
if the user does not exist.
list<Item> getUserRatedItems(CLIENT_KEY, long userId, list<string> displayColumns)
: Takes the client key, the user id of the user you want to retrieve
rated items for, and a list of column names you want to display information about the item. Returns a list of Item
objects that represent items the user has rated.
list<Item> getSearchItems(CLIENT_KEY, string query, list<string> columnsToSearch, list<string> displayColumns)
: Takes your client key, the search query your user searched,
a list of columns to perform the search, and the list of names of columns you want to display for each item. Returns a list of Item
objects that match your search query.
list<Item> makeOverallRatingBasedOrRandomRecommendation(CLIENT_KEY, string rankColumnName, long numRecs, list<string> displayColumns)
: Takes your client key,
the column name of the column containing the ratings for each item, the number of recommendations you want to make, and a list of column names of info you want to display for each item.
Return a list of Item
objects that were chosen using provided numerical rankings or if you did not provide numerical ratings, a list of random items.
If you login to your Kibitz account, you will be giving the option of linking to a ratings table prepopulated with individual user ratings of item or a similarity table for item based recommendations. Your tables must reside in the same repository as your items table. Your ratings table should contain a column of primary keys of items, a column of user ids, and a column of user ratings. The following is an example of a valid table:
item_id, user_id, rating 12, 10, 5 1 , 10, 7 2, 10, 8 4, 3 , 6
(COMING SOON!) You may also choose to link an item similarity table for item-based recommendations. Your table must contain two columns of item ids (primary keys) and a column of the numerical similarity between the two items (the numbers can be arbitrarily large or small). The following is an example of a valid item similarity table:
item_id_1, item_id_2, similarity 1, 2, 0.9 1, 3, 0.8 1, 5, 1.0 2, 4, 0.2 3, 4, 0.5