MIGHTY JOE YOUNG (1998)
Rating: 3 stars (out of 4.0) ******************************** Key to rating system: 2.0 stars - Debatable 2.5 stars - Some people may like it 3.0 stars - I liked it 3.5 stars - I am biased in favor of the movie 4.0 stars - I felt the movie's impact personally or it stood out ********************************* A Movie Review by David Sunga
Directed by: Ron Underwood
Written by Lawrence M. Konner and Mark Rosenthal
Synopsis: An orphaned girl named Jill Young (Charlize Theron) has befriended an orphaned baby gorilla named Joe who carries within his genes the DNA for gigantism. Joe's parents were killed by poachers. Years later, Jill is a young woman living in Africa, and her animal friend Joe is 4.6 meters tall and weighs 905 kilograms, a magnificent, gentle, and intelligent giant. For Joe's safety, Jill and her friend Gregg (Bill Paxton) establish a place for Joe in the United States at a game reserve called the California Animal Conservancy.
But the evil Mr. Strasser (Rade Serbedzija) and his men, the diabolical poachers who killed Joe's parents, plot to deceive Jill and take Joe from the Conservancy in order to sell him on the environmental black market. When Joe escapes into the Hollywood area, the poor beast is hunted by gung ho police snipers, evil poachers, and the good guys as well. Who will reach him first?
Opinion: As far as I know, there are two main big gorilla flicks: MIGHTY JOE YOUNG of 1949, and the well known 1933 KING KONG. MIGHTY JOE YOUNG (1998) is a modernized remake of the 1949 classic.
MIGHTY JOE YOUNG (1998) is a family movie made especially for children, and it succeeds very well. The good guys are good (young, friendly, with light colored clothing); the bad guys are bad (dark hat, suspicious accent, black gloves to hide identifiable hand), and; the beloved animal is a misunderstood friend who has run away from home. My neighbor took with her with a troop of four young kids, and they never failed to clap and cheer when the good guys got one over on the bad guys. The kids also held their breath when it looked like big Joe might be hurt. It's obvious that cast members Charlize Theron and Bill Paxton love animals or have pets of their own. Both Theron and Paxton do such a terrific acting job that you forget Joe is only a special effect and not an animal actor.
The adult theme of MIGHTY JOE YOUNG concerns the protection of the endangered Gorilla, thought to be a mythical creature until proven to be real by a skin and skull in 1849. Although fairly intelligent and man-like (Koko the sign language gorilla was tested to have an IQ over 80), the animal has been hunted to near extinction. Today there are less than 400 remaining wild mountain gorillas. As for the possibility of gigantism, modern gorillas can weigh 300 to 400 pounds (136 to 181 kg). The largest ape, "Gigantopethecus," lived in Asia during the Pleistocene Era. From its bones it is estimated to have been approximately 10 feet tall and have weighed over 600 pounds (271 kg). It is almost certainly extinct, but some suggest Gigantopethecus is responsible for tales of the Yeti and Bigfoot.
Reviewed by David Sunga December 20, 1998
Copyright © 1998 by David Sunga This review and others like it can be found at THE CRITIC ZOO: http://www.criticzoo.com email<EMAIL_ADDRESS>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


justkarenlo
WEB DEVELOPER
I'm a Dev Bootcamp graduate who has learned how to build web applications using Ruby and JavaScript, but most importantly, I've learned how to learn. My experiences have taught me that with enough diligence, patience, mentorship, and Googling I can get through any challenge.


{% extends "base/base.html" %}
{% block content %}
please fill out this form so we can get your shipping address and other needed data, then proceed to the payment page
{% if request.user.is_authenticated == False %}
please note that if you are not logged in you will not be able to look at your order history later on


“If you torture the data long enough, it will confess.” – Ronald Coase
Long experience in project management for large accounts in retail, bank-finance, insurance, energy & power, industry and corporate. National industrial and commercial sectors in Spain, Europe and LATAM.
In the last 10 años he impartido +5000 hours in:
business school:
universities:
other:
eLearning platform:
Other collaborations and in-company training in Machine Learning, Data Analytics and Digital Analysis-Tag Management and Data Visualization.
Bootcamp of Data Science & Machine Learning Engineering
Master in Business Intelligence / course on Business Intelligence, ETL, Power BI, SSAS, Data Integration at CICE Business School
Bootcamp of Data Science and Data Analytics, Machine Learning, Deep Learning and Big Data in ** Master in Data Science Neoland ** face-to-face and online in Madrid.
Data Science and Deep Learning Bootcamp in ARTIFICIAL INTELLINGE SCHOOL
Data Analysis, Google Tag Manager and Digital Analytics classroom / distance courses at KPIschool.
Machine Learning Databricks, 2020
Databricks
Data Engineering for Data Science, 2019
UOC University
MEng in Artificial Intelligence, 2018
Massachusetts Institute of Technology
BSc in Artificial Intelligence, 2017
Massachusetts Institute of Technology
100%
70%
60%
99%
20%
50%
100%
60%
30%


Specify the command-line arguments using a single, multi-line string.
Note the following example:
import com.concurrentthought.cla._
val args: Args = """
|java -cp ... foo
|Some description
|and a second line.
| [-i | --in | --input string] Path to input file.
| [-o | --out | --output string=/dev/null] Path to output file.
| [-l | --log | --log-level int=3] Log level to use.
| -p | --path path Path elements separated by ':' (*nix) or ';' (Windows).
| [-q | --quiet flag] Suppress some verbose output.
| [--things seq([-|])] String elements separated by '-' or '|'.
| others Other stuff.
|Any additional help description lines,
|which also have no leading whitespace.
|""".stripMargin.toArgs
The format, as illustrated in the example, has the following requirements:
Zero or more leading and trailing lines without opening whitespace are
interpreted as the "program invocation" string in the help message,
followed by zero or more description lines, which will be concatenated
together (separated by whitespace) in the help message.
Each option appears on a line with leading whitespace.
Each option has one or more flags, separated by "|". As a special case,
one option can have no flags. It is used to provide a help message for all other
command-line tokens that aren't associated with flags (which will be stored
in Args.remaining).
Note that these tokens are handled whether or not you specify a line like
this or not.
If the option is not required for the user to specify a value, the flags
and name must be wrapped in "[...]". Specifying a default value is effectively
the name as not required, but the main purpose is display the expected
behavior to the user.
After the flags, the "middle column" describes the type of option and
optionally a default value. The types correspond to several helper functions
in Opt: string, byte, char, int, long, float, double, path,
seqString, where the string "seq" is used, followed by a required
(delim) suffix to specify the delimiter regex as shown in the example, and
flag which indicates a boolean flag where no value is expected, but the
flag's presence means true and absence means false.
However, for a no-flag option, the value in this column is interpreted as a
name for the option for the help message. This is the one case where the string
isn't interpreted as a type specifier.
The = indicates the default value to use, if present. Current limitations
of the type specification include the following: (i) Opt.seq[V] isn't
supported in this mechanism, (ii) default values can't be specified for
the path or seq types, nor for the no-flag case (an implementation limitation).
The remaining text is interpreted as the help string.
Note
This is an experimental feature. There are several known limitations:
It provides no way to use the predefined flags like Args.quietFlag,
although Args.helpFlag and Args.remainingOpt are automatically added
if the list of options doesn't explicitly define help and no-flag constructs.
The general case of Opt.seq[V] isn't supported, only Opt.seqString.
Specify the command-line arguments using a single, multi-line string. Note the following example:
The format, as illustrated in the example, has the following requirements:
Args.remaining
). Note that these tokens are handled whether or not you specify a line like this or not.Opt
:string
,byte
,char
,int
,long
,float
,double
,path
,seqString
, where the string "seq" is used, followed by a required(delim)
suffix to specify the delimiter regex as shown in the example, andflag
which indicates a boolean flag where no value is expected, but the flag's presence meanstrue
and absence meansfalse
. However, for a no-flag option, the value in this column is interpreted as a name for the option for the help message. This is the one case where the string isn't interpreted as a type specifier. The=
indicates the default value to use, if present. Current limitations of the type specification include the following: (i)Opt.seq[V]
isn't supported in this mechanism, (ii) default values can't be specified for thepath
orseq
types, nor for the no-flag case (an implementation limitation).This is an experimental feature. There are several known limitations:
Args.quietFlag
, althoughArgs.helpFlag
andArgs.remainingOpt
are automatically added if the list of options doesn't explicitly define help and no-flag constructs.Opt.seq[V]
isn't supported, onlyOpt.seqString
.


The extracted properties and features and sent to the 2Nice server for MAP inference which calculates the most likely assignment of unknown properties.
Results are visualized in the graph (Conditional Random Field) below where the known properties are red nodes, unknown properties are blue nodes and the edges are features.
Edge labels show the feature name together with the score of the best assignment (higher score is better).
Features with score 0 are those not seen in the training data (this happens for example with features relating function name to variables).
In the last step we simply take the best labels computed from MAP inference and use them to replace the corresponding variables in the original JavaScript program.


education appointment form
Course Type
Society
Language
How would you like to bo located ?
By phone
By email
Hours : 8am 10pm
Hours : 9am 11pm
I agree to the
Terms and Conditions


class: center, middle, inverse, title-slide # Unsupervised Learning ## Machine Learning in Economics ### Hüseyin Taştan ### Yildiz Technical University --- class: my-large-font, inverse, middle, center, clear <style type="text/css"> .remark-slide-content { font-size: 28px; padding: 1em 4em 1em 4em; } .my-large-font { font-size: 40px; } .my-small-font { font-size: 25px; } .my-medium-font { font-size: 30px; } .red { color: red; } </style> # Lecture Plan [Supervised vs Unsupervised Learning](#unsup) [Principal Components Analysis](#pca) [Clustering with K-Means Algorithm](#kmeans) [Hierarchical Clustering](#hc) --- name: unsup # Unsupervised Learning .pull-left[ ![](img/unsuper1.PNG) * So far in this course, we only focused on supervised learning methods. In supervised learning, both response variable `\(Y\)` features `\(X\)` are observed together ] -- .pull-right[ * In unsupervised learning, the output variable `\(Y\)` (or sometimes called labels) are not available. We only observe `\(X\)` variables. * Because we don't have a response variable, the purpose is not to make predictions. * Instead, we aim to discover hidden subgroups and relationships among a set of variables and visualizing the data in an informative way. ] --- # Unsupervised Learning * Unsupervised Learning methods can be used as part of an Exploratory Data Analysis (EDA) * EDA involves summarizing and analyzing data sets by numerical and graphical tools in order to gather insights and better understand the relationships among variables. * In practice, the most widely used unsupervised learning methods are 1. **Principal Components Analysis (PCA)** 1. **Clustering** --- # Unsupervised Learning 1. **Principal Components Analysis (PCA)**: - Useful to visualize high-dimensinoal data sets, to reduce dimensions, and to pre-process data before supervised learning (e.g., PCA regression) 1. **Clustering**: - Uncover homogenous subgroups especially in large data sets - For example, grouping online customers based on personal characteristics and shopping habits - Clustering products based on consumer reviews and comments - Clustering patients based on genetic traits --- class: my-large-font, inverse, middle, center, clear Principal Components Analysis --- name: pca # Principal Components Analysis (PCA) * What are principal components? -- * Let's assume that we have a set of `\(p\)` features, `\((X_1, X_2,\ldots,X_p)\)`, and we wish to draw scattor plots for each pairs * How many scatter plots do we have? -- * Answer = `\(p(p-1)/2\)`. For example, when `\(p=10\)` we have 45, when `\(p=20\)` we have 190! * Alternatively, we can find a two dimensional representation of data that contain the most variation in the data. * PCA: a low-dimensional representation of data that contains most of the variability in the data set --- class: clear Province data in Turkey (first 10 features) ![:scale 90%](img/scatter1.PNG) --- # Calculating Principal Components * **The first principal component**, `\(Z_1\)`, is the normalized linear combination of the features that has the largest variance: `$$Z_{1}=\phi_{11} X_{1}+\phi_{21} X_{2}+\ldots+\phi_{p 1} X_{p}$$` * Normalization means that squared weights, `\(\phi\)` (**factor loadings**), sum to unity: `$$\sum_{j=1}^{p} \phi_{j1}^2 = 1,~~~~\underbrace{\phi_{11},\phi_{21},\ldots,\phi_{p1}}_{\mbox{weights of first PC}}$$` * The vector of weights for the first principal components: `$$\phi_1 = (\phi_{11},\phi_{21},\ldots,\phi_{p1})^T$$` --- # Calculating Principal Components The first principal component can be found by solving the following optimization problem (after standardizing each variable) `$$\max_{\phi_{11}, \ldots, \phi_{p 1}}\left\{\frac{1}{n} \sum_{i=1}^{n}\left(\sum_{j=1}^{p} \phi_{j 1} x_{i j}\right)^{2}\right\} \text { subject to } \sum_{j=1}^{p} \phi_{j 1}^{2}=1$$` .pull-left[ ![:scale 90%](img/pca10.PNG) ] .pull-right[ First PC: is the direction at which data have the highest variation. In this example, `\(\phi_{11}=0.839\)`, `\(\phi_{21} = 0.544\)` `$$z_{i1} = 0.839~ \mbox{Population}_i + 0.544~ \mbox{Ad Spending}_i$$` ] --- # Calculating Principal Components .pull-left[ ![:scale 90%](img/pca10.PNG) ] .pull-right[ - **Second Principal Component**: is the linear combination of variables that has maximal variance among all linear combinations that are uncorrelated with the first principal component. In the example: `\(\phi_{12}=0.544\)`, `\(\phi_{22} = -0.839\)` ] `$$z_{i2} = 0.544~ \mbox{Population}_i - 0.839~ \mbox{Ad Spending}_i$$` - The second principal component is orthogonal to the first principal component. - This means their correlation is 0, in other words, the angle between them is 90 degrees. --- # Proportion of Variance Explained (PVE) * How much of the total variation in data can be explained by principal components? * The total variance in a data set that is centered to have mean zero is `$$\sum_{j=1}^{p} \mbox{Var}(X_j)=\sum_{j=1}^{p} \frac{1}{n} \sum_{i=1}^{n} x_{ij}^2$$` - The variance explained by the `\(m\)`th principal component is `$$\frac{1}{n} \sum_{i=1}^{n} z_{i m}^{2}=\frac{1}{n} \sum_{i=1}^{n}\left(\sum_{j=1}^{p} \phi_{j m} x_{i j}\right)^{2}$$` --- # Proportion of Variance Explained (PVE) Proportion of Variance Explained (PVE) is defined as `$$PVE_m=\frac{\sum_{i=1}^{n}\left(\sum_{j=1}^{p} \phi_{j m} x_{i j}\right)^{2}}{\sum_{j=1}^{p} \sum_{i=1}^{n} x_{i j}^{2}}$$` - The PVE of each principal component is a positive quantity - We can also compute the cumulative PVE over the first few principal components - The PVEs sum to 1. - Using the scree plot we can decide how many principal components to use in order to explain the most of the variation in the data. --- # Scree (PVE plot) ![:scale 90%](img/pca2.PNG) The plot of the PVEs with respect principal components is called the PVE plot or the scree plot. In the graph above, the first two principal components explain about 87% of the variance. --- # Principal Components Regression - The principal components regression (PCR) approach is a dimension reduction technique - It involves constructing `\(M\)` principal components, `\(Z_1,Z_2,\ldots,Z_M\)`, and then using these as the predictors in a linear regression model. - In cases where we have large number of predictors, `\(p\)`, a smaller number of principal components, `\(M<<p\)`, may be sufficient to explain most of the variability in the data. - In cases where the first few principal components suffice to explain the most variation in the data, the PCR approach may perform well. - In practice, the number of principal components to be used in the regression can be chosen by cross-validation. --- # Clustering Methods * The purpose of clustering methods is to find subgroups or clusters that contain homogenous observations. * In a good clustering, observations in a subgroup are similar to each other but very different across subgroups. * Example: market segmentation. Suppose we have access to a large number of measurements (e.g. median household income, occupation, distance from nearest urban area, and so forth) for a large number of people. - Our goal is to perform market segmentation by identifying subgroups of people who might be more receptive to a particular form of advertising, or more likely to purchase a particular product. --- # Clustering Methods - The task of performing market segmentation amounts to clustering the people in the data set. - There are many different types of clustering methods * The most widely used methods in practice: * K-Means Clustering * Hierarchical Clustering --- class: my-large-font, inverse, middle, center, clear name: kmeans K-Means Clustering --- # K-Means Clustering * Purpose: identify `\(K\)` distinct, non-overlapping clusters in a data set. ![:scale 90%](img/kmeans1.PNG) --- # K-Means Clustering - Partition data into `\(K\)` clusters: `\(C_1, C_2,\ldots,C_K\)` - Each observation belongs to at least one of the `\(K\)` clusters; that is, `\(C_{1} \cup C_{2} \cup \ldots \cup C_{K}=\{1, \ldots, n\}\)` - Clusters are non-overlapping; that is, `\(C_{k} \cap C_{k^{\prime}}=\emptyset\)`, for all `\(k\neq k^{\prime}\)`. - K-Means Clustering: make within-cluster variation as small as possible. The optimization problem is `$$\min_{C_{1}, \ldots, C_{K}}\left\{\sum_{k=1}^{K} \frac{1}{\left|C_{k}\right|} \sum_{i, i^{\prime} \in C_{k}} \sum_{j=1}^{p}\left(x_{i j}-x_{i^{\prime} j}\right)^{2}\right\}$$` where `\(\left|C_{k}\right|\)` = the number of obs. in cluster `\(k\)`. We minimize the sum of all pair-wise squared Euclidean distances between the observations in each cluster. --- class: my-small-font # K-Means Clustering .pull-left[ ![](img/kmeans2.PNG) ] .pull-right[ Steps: 1. Randomly assign each observation to one of `\(K\)` clusters. 2. Iterate the following steps until convergence: 2a. For each of the `\(K\)` clusters, compute the cluster centroid. The `\(k\)`th cluster centroid is the vector of the `\(p\)` feature means for the observations in the `\(k\)`th cluster. 2b. Assign each observation to the cluster whose centroid is closest (where “closest” is defined using Euclidean distance. ] --- # K-Means Clustering: Illustration .pull-left[ ![](img/kmeans3.PNG) ] .pull-right[ - Step 1 - Randomly assign each observation to one of `\(K\)` clusters. ] --- # K-Means Clustering: Illustration .pull-left[ ![](img/kmeans4.PNG) ] .pull-right[ - Iteration 1, Step 2-a - compute the cluster centroids (shown in colored circles) - In the first iteration, the centroids are almost completely overlapping because the initial cluster assignments were chosen at random. ] --- # K-Means Clustering: Illustration .pull-left[ ![](img/kmeans5.PNG) ] .pull-right[ - Iteration 1, Step 2-b - Assign each observation to the closest centroid ] --- # K-Means Clustering: Illustration .pull-left[ ![](img/kmeans6.PNG) ] .pull-right[ - Iteration 2, Step 2-a - Compute new cluster centroids * Step 2-b - Repeat cluster assignment * Go back and repeat ] --- # K-Means Clustering: Illustration .pull-left[ ![](img/kmeans7.PNG) ] .pull-right[ After 10 iterations * No further improvement * But the solution to K-means algorithm is the local optimum and it may not be the best solution * This means that if we run the algorithm with different initial conditions the solution may be different ] --- # K-Means Clustering: Illustration .pull-left[ ![](img/kmeans8.PNG) (ISLR, Fig-10.7, p.390) ] .pull-right[ * This graph displays 6 solutions with different initial conditions. * The objective function values are shown on top of the plots. * We see that the best solution is the one with the objective function value 235.8 (Note: the solutions 2-3-4-5 are the same despite different colors) ] --- class: my-large-font, inverse, middle, center, clear name: hc Hierarchical Clustering --- # Hierarchical Clustering * K-Means clustering requires choosing the number of clusters. The solution may be sensitive to `\(K\)` - If `\(K\)` is unknown or we don't want to specify it we may apply Hierarchical Clustering as an alternative. * We don't need to specify `\(K\)` in hierarchical clustering. Also, we can visualize the clustering structure using a tree diagram known as **dendrogram**. - The most common type of hierarchical clustering is the bottom-up or agglomerative clustering. - In this approach a dendrogram is built starting from the leaves and combining clusters up to the trunk. --- # Bottom-Up Approach .pull-left[ ![](img/hier1.png) ] .pull-right[ * Builds a clustering hierarchy in a bottom-up fashion (from leaves -observations- to branches) ] --- # Bottom-Up Approach .pull-left[ ![](img/hier2.png) ] .pull-right[ * Builds a clustering hierarchy in a bottom-up fashion (from leaves -observations- to branches) * Finds closest observations and merges them into a cluster. ] --- # Bottom-Up Approach .pull-left[ ![](img/hier3.PNG) ] .pull-right[ * Builds a clustering hierarchy in a bottom-up fashion (from leaves -observations- to branches) * Finds closest observations and merges them into a cluster. ] --- # Bottom-Up Approach .pull-left[ ![](img/hier4.PNG) ] .pull-right[ * Builds a clustering hierarchy in a bottom-up fashion (from leaves -observations- to branches) * Finds closest observations and merges them into a cluster. * Finds and merges closest clusters together. ] --- # Bottom-Up Approach .pull-left[ ![](img/hier5.PNG) ] .pull-right[ * Builds a clustering hierarchy in a bottom-up fashion (from leaves -observations- to branches) * Finds closest observations and merges them into a cluster. * Finds and merges closest clusters together. * At the end we obtain a single cluster consisting of all observations. ] --- # Bottom-Up Approach: Dendrogram .pull-left[ ![](img/hier6.PNG) * The height of the dendrogram shows how different two observations are. * Observations that fuse earlier are relatively more similar to each other. ] .pull-right[ * Dendrogram is like an upside-down tree. Leaves (observations) are at the bottom. * We go from bottom to top by clustering closer observations and groups together. - Note that the number of clusters decreases as we move from bottom to top. ] --- # Example .pull-left[ ![](img/hier7.PNG) ] .pull-right[ * This graph shows simulated data with two features - `\(n=45\)`, `\(p=2\)`, `\(K=3\)`, and we know which cluster each observation belongs (see ISLR, p.391). * Let's assume that we don't know the group labels and apply hierarchical clustering and draw the dendrogram. ] --- # Dendrogram .left-column[ * Left: observations (green) * Middle: 2 clusters * Right: 3 clusters ] .right-column[ ![:scale 100%](img/hier8.PNG) Note that the number of clusters is determined by the break point (dashed vertical line) ] --- # Dendrogram Example .pull-left[ ![:scale 70%](img/hier9.PNG) ![:scale 70%](img/hier10.PNG) ] .pull-right[ - Observations (5,7) and (1,6) are fused together at the bottom because they are closer to each other. - In the dendrogram, observations (2,9) seem to be close to each other. Are they? - No. From the scatter plot we see that obs. 9 is far from 2. - Use vertical axis to assess similarity of observations not the horizontal axis. ] --- class: my-small-font # Hierarchical Clustering Algorithm .pull-left[ ![](img/hier12.PNG) ] .pull-right[ - Start with each point as a separate cluster ( `\(n\)` clusters) - Define a dissimilarity measure (e.g., Euclidean distance) - Fuse two clusters that are most similar so that there are now `\(n-1\)` clusters. - Next fuse again the two clusters that are most similar so that there now are `\(n-2\)` clusters. - Proceed iteratively until all of the observations belong to one single cluster. ] --- # Hierarchical Clustering Algorithm: Illustration .pull-left[ ![](img/hier11.PNG) ] .pull-right[ * Initially there are 9 distinct clusters. * Using the dissimilarity measure, fuse (5,7) together into a cluster. ] --- # Hierarchical Clustering Algorithm: Illustration .pull-left[ ![](img/hier13.PNG) ] .pull-right[ * Initially there are 9 distinct clusters. * Using the dissimilarity measure, fuse (5,7) together into a cluster. * Next fuse together observations (6,1) ] --- # Hierarchical Clustering Algorithm: Illustration .pull-left[ ![](img/hier14.PNG) ] .pull-right[ * Initially there are 9 distinct clusters. * Using the dissimilarity measure, fuse (5,7) together into a cluster. * Next fuse together observations (6,1) * Next, fuse together cluster (5,7) with cluster 8. * Iterate until a single cluster * How do we know that we need to fuse cluster (5,7) with cluster 8? ] --- # Hierarchical Clustering Algorithm .pull-left[ ![](img/hier9.PNG) ] .pull-right[ * We need to extend the dissimilarity measure to the case where we can compare clusters that contain multiple observations. * The notion of **linkage** can be used for this purpose. It defines the dissimilarity between two groups of observations. * In practice the following linkage types are used: complete, average, single, centroid. ] --- class: my-small-font # Linkage Types (Table 10.2, p. 395) 1. **Complete linkage**: Maximal intercluster dissimilarity. Compute all pairwise dissimilarities between the observations in cluster A and the observations in cluster B, and record the largest of these dissimilarities. 1. **Single linkage**: Minimal intercluster dissimilarity. Compute all pairwise dissimilarities between the observations in cluster A and the observations in cluster B, and record the smallest of these dissimilarities. Single linkage can result in extended, trailing clusters in which single observations are fused one-at-a-time. 1. **Average linkage**: Mean intercluster dissimilarity. Compute all pairwise dissimilarities between the observations in cluster A and the observations in cluster B, and record the average of these dissimilarities. 1. **Centroid linkage**: Dissimilarity between the centroid for cluster A (a mean vector of length p) and the centroid for cluster B. Centroid linkage can result in undesirable inversions. --- # Types of Linkage: Illustration ![:scale 75%](img/linkages0.PNG) <small>(Source: ISLR Fig-10.12, p.397)<small> --- class: my-small-font # Choice of Dissimilarity Measure .pull-left[ ![:scale 100%](img/hier15.PNG) ] .pull-right[ * The choice of the dissimilarity measure affects the shape of the dendrogram. * So far we used Euclidean distance as a measure of dissimilarity. * In some cases, correlation-based distance measures may be more useful. * `\(n=3\)` observations on `\(p=20\)` variables are shown in the graph. (Source: ISLR Fig-10.13, p.398) ] - In terms of Euclidean distance, observations 1 and 3 are similar - However, observations 1 and 2 are highly correlated so they would be considered similar in terms of a correlation-based dissimilarity measure --- # Summary and Practical Issues in Clustering * Clustering algorithms are the most important tools used in unsupervised learning. * To apply these algorithms, we have to make the following decisions: * In both K-means and hierarchical clustering, the results may be sensitive to the units of measurement. Should the variables be standardized (to have zero mean and unit variance, say)? (for an example see ISLR, p. 398-9) - In the case of hierarchical clustering: - What dissimilarity measure should be used? What type of linkage should be used? Where should we cut the dendrogram in order to obtain clusters? - In the case of K-means clustering: - How many clusters should we look for the data? --- # Summary and Practical Issues in Clustering * In practice, we can try several different choices, and look for the one with the most useful or interpretable solution. - We need to be careful about reporting the results from clustering analysis - These results should not be taken as the absolute truth about a data set. - Often clustering algorithms are sensitive (not robust) to random perturbations of data. (can obtain completely different results after removing a random portion of the data set) - Rather, these results should be used as a starting point for the development of a scientific hypothesis and further study, preferably on an independent data set. (ISLR, p. 401)


| AR-EL Workshop Equipment Ltd ..... Registered in Scotland : No 204369 ..... VAT Registration No : 743 3475 30 SALES AND DELIVERY TO THE UNITED KINGDOM ONLY
ENTER ONLINE STORE
|
Spanners
Compressors
Pliers
Welders
|
|
Space Heaters
Gadgets and Gifts
Jacks
Miscellaneous
|
|
Power Tools
Drill Bits
Screwdrivers
Hex and Torx Tools
|
|
Socket Sets
Power Washers
Vices
Gadgets and Gifts
|
|


The object of Battleship is to try and sink all of the other player's before they
sink all of your ships. All of the other player's ships are somewhere on his/her board.
You try and hit them by calling out the coordinates of one of the squares on the board.
The other player also tries to hit your ships by calling out coordinates. Neither you
nor the other player can see the other's board so you must try to guess where they are.
Each board in the physical game has two grids: the lower (horizontal) section for the player's
ships and the upper part (vertical during play) for recording the player's guesses.
Each player places the 5 ships somewhere on their board.
The 5 ships have the size of 2-6 fields.
The ships can only be placed vertically or horizontally.
Diagonal placement is not allowed. No part of a ship may hang off the edge of the board.
Ships may not overlap each other. No ships may be placed on another ship.
Once the guessing begins, the players may not move the ships.


The ME Association (The MEA), founded in 1976, funds and supports research and provides information and support, education and training. In this role, we benefit people with ME (myalgic encephalopathy)/CFS (chronic fatigue syndrome), professionals and all others interested in the illness. Sign up to our electronic newsletter by cliking on the "Sign up" tab or find our contact details on the "Contact us" page.


[§27-53] Annual report. The board shall prepare an annual report on progress towards key community outcomes adopted by the legislature, which shall be transmitted to the governor, the legislature, and the public. [L 1999, c 160, §15]


|
LCDGFX LCD display driver
1.0.4
This library is developed to control SSD1306/SSD1325/SSD1327/SSD1331/SSD1351/IL9163/PCD8554 RGB i2c/spi LED displays
|
#include <menu.h>
Public Member Functions | |
| NanoFixedWidthMenu (const NanoPoint &pos, const NanoPoint &size) | |
| void | draw () override |
| Public Member Functions inherited from NanoMenu< T > | |
| NanoMenu (const NanoPoint &pos) | |
| NanoMenu () | |
| void | add (NanoObject< T > &item) |
| void | insert (NanoObject< T > &item) |
| NanoObject< T > * | getSelected () |
| void | down () |
| void | up () |
| Public Member Functions inherited from NanoObjectList< T > | |
| NanoObject< T > * | getNext (NanoObject< T > *prev=nullptr) |
| NanoObject< T > * | getPrev (NanoObject< T > *curr=nullptr) |
| void | update () override |
| void | refresh () override |
| void | draw () override |
| bool | has (NanoObject< T > &object) |
| void | add (NanoObject< T > &object) |
| void | insert (NanoObject< T > &object) |
| void | remove (NanoObject< T > &object) |
Additional Inherited Members |
Class implements menu, organized as the list. Each item has the same some fixed width common for the whole menu.
|
inline |
Creates instance of NanoFixedWidthMenu.
| pos | top left position of menu |
| size | width and height of the menu |
|
inlineoverride |
Draw all menu items in NanoEngine buffer


Audio Hobbyist Taiwan Form
Dear fellow Audio Hobbyist Taiwan, welcome to our survey form! Please fill in the information below to support our database.
Dear fellow Audio Hobbyist Taiwan, welcome to our survey form! Please fill in the information below to support our database.


Docker in NutShell
Docker is an open platform for developing, shipping, and running applications. Docker is designed to deliver your applications faster. With Docker you can separate your applications from your infrastructure.
Docker does this by combining kernel containerization features with workflows and tooling that help you manage and deploy your applications. At its core, Docker provides a way to run almost any application securely isolated in a container. The isolation and security allow you to run many containers simultaneously on your host.
Docker’s container-based platform allows for highly portable workloads. Docker containers can run on a developer’s local host, on physical or virtual machines in a data center, or in the Cloud.
What are the major Docker components?
Docker has two major components:
- Docker Engine: the open source containerization platform.
- Docker Hub: our Software-as-a-Service platform for sharing and managing Docker containers.
What is Docker’s architecture?
Docker uses a client-server architecture. The Docker client talks to the Docker daemon, which does the heavy lifting of building, running, and distributing your Docker containers. Both the Docker client and the daemon can run on the same system, or you can connect a Docker client to a remote Docker daemon.
Docker Architecture Diagram
- The Docker daemon: As shown in the diagram above, the Docker daemon runs on a host machine. The user does not directly interact with the daemon, but instead through the Docker client.
- The Docker client: The Docker client, in the form of the docker binary, is the primary user interface to Docker. It accepts commands from the user and communicates back and forth with a Docker daemon.
- Inside Docker: To understand Docker’s internals, you need to know about three resources:
- Docker images:A Docker image is a read-only template. For example, an image could contain an Ubuntu operating system with Apache and your web application installed. Images are used to create Docker containers.
- Docker registries: Docker registries hold images. These are public or private stores from which you upload or download images. The public Docker registry is provided with the Docker Hub.
- Docker containers: Docker containers are similar to a directory. A Docker container holds everything that is needed for an application to run. Each container is created from a Docker image. Docker containers can be run, started, stopped, moved, and deleted.
How does a Docker image work?
We’ve already seen that Docker images are read-only templates from which Docker containers are launched. Each image consists of a series of layers. Docker makes use of union file systems to combine these layers into a single image. Union file systems allow files and directories of separate file systems, known as branches, to be transparently overlaid, forming a single coherent file system.
One of the reasons Docker is so lightweight is because of these layers. When you change a Docker image—for example, update an application to a new version— a new layer gets built. Thus, rather than replacing the whole image or entirely rebuilding, as you may do with a virtual machine, only that layer is added or updated. Now you don’t need to distribute a whole new image, just the update, making distributing Docker images faster and simpler.
Every image starts from a Base image, for example ubuntu, a base Ubuntu image, or fedora, a base Fedora image. You can also use images of your own as the basis for a new image.
Docker images are then built from these base images using a simple, descriptive set of steps we call instructions. Each instruction creates a new layer in our image. Instructions include actions like:
- Run a command
- Add a file or directory
- Create an environment variable
- What process to run when launching a container from this image
These instructions are stored in a file called a Dockerfile. A Dockerfile is a text based script that contains instructions and commands for building the image from the base image. Docker reads this Dockerfile when you request a build of an image, executes the instructions, and returns a final image.
FROM biodckr/biodocker:latest
################## BEGIN INSTALLATION ###########################
USER biodocker
RUN ZIP=comet_binaries_2016012.zip && \ wget https://github.com/BioDocker/software-archive/releases/download/Comet/$ZIP -O /tmp/$ZIP && \ unzip /tmp/$ZIP -d /home/biodocker/bin/Comet/ && \ chmod -R 755 /home/biodocker/bin/Comet/* && \ rm /tmp/$ZIP
RUN mv /home/biodocker/bin/Comet/comet.2016012.linux.exe /home/biodocker/bin/Comet/comet
ENV PATH /home/biodocker/bin/Comet:$PATH
WORKDIR /data/
##################### INSTALLATION END ##########################
MAINTAINER Felipe da Veiga Leprevost<EMAIL_ADDRESS>How does a container work?
A container consists of an operating system, user-added files, and meta-data. As we’ve seen, each container is built from an image. That image tells Docker what the container holds, what process to run when the container is launched, and a variety of other configuration data. The Docker image is read-only.
When Docker runs a container from an image, it adds a read-write layer on top of the image (using a union file system as we saw earlier) in which your application can then run.
When you run a container, either by using the docker binary or via the API, the Docker client tells the Docker daemon to run a container.
$ docker run -i -t ubuntu /bin/bash
The Docker Engine client is launched using the docker binary with the run option running a new container. The bare minimum the Docker client needs to tell the Docker daemon to run the container is:
What Docker image to build the container from, for example: ubuntu The command you want to run inside the container when it is launched, for example: /bin/bash


STEP 1.
(Free Consultancy)
(Jun 2015 - sept 2015)
Good level of practice with different difficult level of Programs. Great insight of recursive concepts and data structures.
A Web Developer and Programmer
always into self exploration!
I am 22 years old residing in New Delhi. I am a professional Web developer with strong interest in Data Structure.Currently, I am pursuing a Bachelor's degree from Maharaja Agrasen Institute of Technology in Delhi.
In my free time, i love Surfing internet. I find that whenever I surfing internet ,i can know about this world more and know what happens to my surroundings.Also i am a coins collector.
This is my personal website that helps showcase my recent works. Hope you enjoy going through my website.
"Have the courage to follow your heart and intuition.They somehow know know what you truely want to become."
"Life is inherently risky There is only one big risk you should avoid at all costs, and that is the risk of doing nothing"
"You don't learn to walk by following rules.You learn by doing,and by falling over."
"Happiness is not reaching your goal.Happiness is being on the way"
If you call failures experiments, you can put them in your resume and claim them as achievements.
Bachelor In Technology (2013 - 2017)
One more year to go, before getting graduated as an CSE Engineer from this top college affiliated to Gurur Gobind Singh Indraprastha University.
Senior Secondary and Secondary Schooling (2002 - 2013)
One of the most renowned schools of the city,this institute really helped in realise my potentials.
(Jun 2015 - sept 2015)
Good level of practice with different difficult level of Programs. Great insight of recursive concepts and data structures.
(Jan 2016 - May 2016)
Mobile Responsive website design with Html5, Css (Materialize framework), JS, JQuery and AJAX. Good insight of Python Scripts and Django Framework.
(Jun 2016 - Present)
I serve The Testament by developing web application based products for businesses and consumers.


?6
7
8
9
10
ã
The
New
York
copyright
reserved
botanical
Garden
Det.-rev.
M.
Thiv
(B)
2001
INSTITUTE
OF
JAMAICA
2.
T'C
FLORA
OF
JAMAICA
uJl
Parish:
ST*
THOMAS
Y
ew
o
ft
K
BOTANICAL
EM
G
a
D
R
New
YoiV
Botanical
Garden
ib24b?s
Name:
Llsianthius
lonoifollus
L,
Locality:
Four-mile
Wood,
c.
0*8
mile
due
northeast
of
GRANTS
PEN*
Altitude:
300Ñ500
ft*
Date:
February
8f
1980
Habitat:
Thickets
along
roedside
limestone
ledges*
(Erect
herb
1*5
m*
tall)
iVoucher
for
Notes:
1corona
greenish-yellow,
\cencer
studies.
Collector:
George
R.
Proctor
no.
38575
1624675
Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high


yggdrai = initialize and execute a YggdrAI processing tick
local yggdrai = dofile('./AI/USER_AI/yggdrai.lua')
yggdrai(path_to_profile, my_gid)
The yggdrai() function initializes, during its first call, and executes a YggdrAI processing tick.
Loading yggdrai.lua also declares functions such as getActor, newActor, getActors, newGroup to Lua's global envionment. require() is also changed upon yggdrai.lua loading to a more version-friendly function.
During its first call, yggdrai() loads a profile file specified by path_to_profile and generates an actor object from my_gid. This actor object is passed as argument to the functions defined by profile.
yggdrai() also manages the state transitions and the command handlers defined by profile. Each processing tick executes the current state transition function and the current stage function.
Edii Knorand - <https://github.com/ediiknorand/>


Hi there! My name is Ashika and I'm a Computer Science student at MIT.
This year, I'll be getting my Masters of Engineering (M.Eng) after
spending a summer doing front-end engineering at
Kensho. In my freetime, you can catch
me singing and learning how to produce music or doing Chloe
Ting workouts 😤.
Well hello there! My name is Ashika and I still need to fill this out!
Feel free to
email me with details!
Alright, since you've read this far, have a joke: Why do programmers always mix up Halloween and Christmas?
Because Oct. 31 == Dec. 25!


/**
* Read a previously recorded training set
* @warning - the class is not yet implemented
*/
class TrainingSetReader {
constructor() {
// use lfo reader internally
}
play(exampleIndex) {
}
pause() {
}
stop() {
}
}
export default TrainingSetReader;


Home
Documentation
Google Group
Get Involved
About
DataGenerator
Real Data.
Insanely Fast.
GitHub
Download
Try Now!
© Copyright 2014
DataGenerator
Contributors.


Login to VideoStorm.io with Facebook or Google, upload a video and share in a flash. No size limit, just lighting fast.
Got any problems or questions, we are here to help anytime. Just send an email, and we will get right back to you.
hello@videostorm.io


|
Cannon Cpp
1.0.0
|
An octree, only used by Trimesh shape currently. More...
#include <Octree.h>
Public Member Functions | |
| Octree (const AABB &aabb, int maxDepth=8) | |
| Octree (int maxDepth=8) | |
| Public Member Functions inherited from OctreeNode | |
| void | reset () |
| bool | insert (const AABB &aabb, int elementData, int level=0) |
| Insert data into this node. More... | |
| void | subdivide () |
| Create 8 equally sized children nodes and put them in the .children array. More... | |
| std::vector< int > & | aabbQuery (const AABB &aabb, std::vector< int > &result) |
| Get all data, potentially within an AABB. More... | |
| std::vector< int > & | rayQuery (Ray *ray, const Transform &treeTransform, std::vector< int > &result) |
| Get all data, potentially intersected by a ray. More... | |
| void | removeEmptyNodes () |
| removeEmptyNodes More... | |
| const AABB & | aabb () const |
| void | setAabb (AABB value) |
| std::vector< int > & | data () |
| std::vector< OctreeNode * > & | children () |
Additional Inherited Members | |
| Protected Member Functions inherited from OctreeNode | |
| OctreeNode (OctreeNode *root, const AABB &aabb) | |
| OctreeNode (OctreeNode *root) | |
| virtual | ~OctreeNode () |
| Protected Attributes inherited from OctreeNode | |
| OctreeNode * | _root = nullptr |
| The root node. More... | |
| AABB | _aabb |
| Boundary of this node. More... | |
| std::vector< int > | _data |
| Contained data at the current node level. More... | |
| std::vector< OctreeNode * > | _children |
| Children to this node. More... | |
| int | _maxDepth = 8 |
An octree, only used by Trimesh shape currently.
| Octree::Octree | ( | const AABB & | aabb, |
| int | maxDepth = 8 |
||
| ) |
| Octree::Octree | ( | int | maxDepth = 8 | ) |


Santo Domingo Yanhuitlán, Mexico
Capilla del Calvario (Chapel of the Calvary)
This dome-topped chapel houses the most-venerated religious figure in Yanhuitlán.
by linkogeckoWhen different Catholic orders divided up the territory of New Spain (now Mexico) for evangelization, most of the territory now occupied by the state of Oaxaca was given to the Dominicans.
The Chapel of the Calvary is small but still impressive. It’s also built out of cantera, but since it is fairly modern, its facade is more plain than other similar churches. Featuring circular shapes and the eclectic touch of a dome and roof covered entirely in metallic silver paint, it is the chapel rather than the convent that houses the most-venerated religious figure in Yanhuitlán.
El Divino Señor de Ayuxi is a figure of a Crucifixion made out of carved wood and sugarcane. A yearly celebration sees this religious icon paraded in procession from the Chapel of the Calvary to the Convent of Santo Domingo Yanhuitlán in one of the most important religious feasts of the town.
The Divine Lord is believed to be centuries-old, and there is a historic precedent of a Catholic chapel built on the site of the Calvary Chapel. The current building is more recent, however, and probably dates to the 19th century (although the sci-fi looking done is almost certainly a more recent addition).
Know Before You Go
The Divine Lord of Ayuxi is housed in the Chapel for every month of the year except May, when it is housed in the Convent. This Procession takes place every April 29 and is one the most important feasts in Yanhuitlán.
Visit Mexico with Atlas Obscura Trips
Layer by Layer: A Mexico City Culinary Adventure
Immerse yourself in the complex cultural identity of Mexico City through its street food, bustling markets, and well-seasoned history of gastronomic traditions.
Learn More »
Community Discussion
-
No Comments Yet
Start the discussion.
forum Visit our forums.


qMRLab
Quantitative MRI. Under one umbrella.
Welcome to qMRLab
qMRLab is an open-source software for quantitative MR image analysis. The main goal is to provide the community with an intuitive tool for data fitting, plotting, simulation and protocol optimization for a myriad of different quantitative models. The modularity of the implementation makes it easy to add any additional modules and we encourage everyone to contribute their favorite recipe for qMR!
Data simulator
The simulation interface allows end users to easily simulate qMR data and evaluate how well these models perform under known parameters input, determine the most appropriate acquisition protocol and evaluate how fitting constraints impact the results.
Read moreData fitting and visualization
The data fitting provides a simple interface to import real-world qMR data, fit them using the selected fitting procedure, and visualize the resulting parameter maps. More advanced users could also use the command line tools used in the background by the GUI to include data fitting in their analysis scripts.
Read more


--- layout: default title: About This Site permalink: /about/ description: Credits ---
This site is a product of open-source internet and coding collaboration. It's totally responsive, mobile first, and it wouldn't be here without:


| I l@ve RuBoard |
In C and C++, operators << and >> are used for shifting bits of an integer to the right or the left respectively. The classes basic_istream<> and basic_ostream<> overload operators >> and << as the standard I/O operators. Thus, in C++ the "shift operators" became the "I/O operators."[6]
[6] Some people also call the I/O operators inserters and extractors.
The class basic_ostream (and thus also the classes ostream and wstream) defines << as an output operator. It overloads this operator for all fundamental types, including char*, void*, and bool.
The output operators for streams are defined to send their second argument to the corresponding stream. Thus, the data is sent in the direction of the arrow:
int i = 7; std::cout << i; // outputs: 7 float f = 4.5; std::cout << f; // outputs: 4.5
The << operator can be overloaded such that the second argument is an arbitrary data type. This allows the integration of your own data types into the I/O system. The compiler ensures that the correct function for outputting the second argument is called. Of course, this function should in fact transform the second argument into a sequence of characters sent to the stream.
The C++ standard library also uses this mechanism to provide output operators for strings (see page 524), bitsets (see page 468), and complex numbers (see page 539):
std::string s("hello"); s += ", world"; std::cout << s; // outputs: hello, world std::bitset<10> flags(7); std::cout << flags; // outputs:<PHONE_NUMBER> std::complex<float> c(3.1,7.4); std::cout << c; // outputs: (3.1,7.4)
The details about writing output operators for your own data types are explained in Section 13.12.
The fact that the output mechanism can be extended to incorporate your own data types is a significant improvement over C's I/O mechanism that uses printf(): It is not necessary to specify the type of an object to be printed. Instead, the overloading of different types ensures that the correct function for printing is deduced automatically. The mechanism is not limited to standard types. Thus, the user has only one mechanism that works for all types.
Operator << can also be used to print multiple objects in one statement. By convention, the output operators return their first argument. Thus, the result of an output operator is the output stream. This allows you to chain calls to output operators like this:
std::cout << x << " times " << y << " is " << x * y << std::endl;
Operator << is evaluated from left to right. Thus.
std::cout << x
is executed first. Note that the evaluative order of the operator does not imply any specific order in which the arguments are evaluated; only the order in which the operators are executed is defined. This expression returns its first operand— std::cout. So,
std::cout << " times "
is executed next. The object y, the string literal " is ", and the result of x * y are printed accordingly. Note that the multiplication operator has a higher priority than operator <<, so you need no parentheses around x * y. However, there are operators that have less priority, such as all logical operators. In this example, if x and y are floating-point numbers with the values 2.4 and 5.1, the following is printed:
2.4 times 5.1 is 12.24
The class basic_istream (and thus also the classes istream and wistream) defines >> as an input operator. Similar to basic_ostream, this operator is overloaded for all fundamental types including, char*, void*, and bool. The input operators for streams are defined to store the value read in their second argument. As with operator <<, the data is sent in the direction of the arrow:
int i; std::cin >> i; // reads an int from standard input and stores it in i float f; std::cin >> f; // reads a float from standard input and stores it in f
Note that the second argument is modified. To make this possible, the second argument is passed by nonconstant reference.
Like output operator << it is also possible to overload the input operator for arbitrary data types and to chain the calls:
float f; std::complex c; std::cin >> f >> c;
To make this possible, leading whitespace is skipped by default. However, this automatic skipping of whitespace can be turned off (see page 625).
The standard I/O operators are also defined for types bool, char*, and void*. In addition, you can extend it for your own types.
By default, Boolean values are printed and read numerically: false is converted from and to 0, and true is converted from and to 1. When reading, values different from 0 and 1 are considered to be an error. In this case the ios::failbit is set, which might throw a corresponding exception (see page 602).
It is also possible to set up the formatting options of the stream to use character strings for the I/O of Boolean values (see page 617). This touches on the topic of internationalization: Unless a special locale object is used, the strings "true" and "false" are used. In other locale objects, different strings might be used. For example, a German locale object would use the strings "wahr" and "falsch". See Chapter 14 especially for more details.
When a char or wchar_t is being read with operator >>, leading whitespace is skipped by default. To read any character (whether or not it is whitespace) you can either clear the flag skipws (see page 625) or use the member function get() (see page 608).
A C-string (that is, a char*) is read word wise. That is, when a C-string is being read, leading whitespace is skipped by default and the string is read until another whitespace character or end-of-file is encountered. Whether leading whitespace is skipped automatically can be controlled with the flag skipws (see Section 13.7.7).
Note that this behavior means that the string you read can become arbitrarily long. It is already a common error in C programs to assume that a string can be a maximum of 80 characters long. There is no such restriction. Thus, you must arrange for a premature termination of the input when the string is too long. To do this, you should always set the maximum length of the string to be read. This normally looks something like this:
char buffer [81]; // 80 characters and '\0' std::cin >> std::setw(81) >> buffer;
The manipulator setw() and the corresponding stream parameter are described in detail in Section 13.7.3.
The type string from the C++ standard library (see Chapter 11) grows as needed to accommodate a lengthy string. It is much easier and safer to use the string class instead of char*. In addition, it provides a convenient function for reading line-by-line (see page 493). So, whenever you can avoid the use of C-strings and use strings.
Operators << and >> also provide the possibility of printing a pointer and reading it back in again. An address is printed in an implementation-dependent format if a parameter of type void* is passed to the output operator. For example, the following statement prints the contents of a C-string and its address:
char* cstring = "hello"; std::cout << "string \"" << cstring << "\" is located at address: " << static_cast<void*>(cstring) << std::endl;
The result of this statement might appear as follows:
string "hello" is located at address: 0x10000018
It is even possible to read an address again with the input operator. However, note that addresses are normally transient. The same object can get a different address in a newly started program. A possible application of printing and reading addresses may be programs that exchange addresses for object identification or programs that share memory.
You can use operators >> and << to read directly into a stream buffer and to write directly out of a stream buffer respectively. This is probably the fastest way to copy files by using C++ I/O streams. See page 683 for examples.
In principle it is very easy to extend this technique to your own types. However, to be able to pay attention to all possible formatting data and error conditions, this takes more effort than you might think. See Section 13.12, for a detailed discussion about extending the standard I/O mechanism for your own types.
| I l@ve RuBoard |


Home
Your Profile
Events
Crime
CLAN
Users
Events
Crime
Joe McBerry
Need to make changes?
Go!
Member since 2015
Cars: BMW 2056
Pets: Coco and Jerry
Security Cameras installed
Available as a Guardian
Home is listed as a "Safe Place"


--- layout: post title: 'Auto caching tables: lighweight INFORMATION_SCHEMA access' date: type: post published: false status: draft categories: - MySQL tags: - Hack - INFORMATION_SCHEMA - MyISAM - SQL - Stored routines - Views meta: _edit_last: '2' author: login: shlomi email<EMAIL_ADDRESS>display_name: shlomi first_name: Shlomi last_name: Noach ---
In my previous post I presented auto-caching tables.
My main drive for hacking this was to solve the issue of INFORMATION_SCHEMA: the fear of bringing a server down because of some necessary query over I_S.
As a matter in fact, there are two problems with heavyweight INFORMATION_SCHEMA tables (such as TABLES, COLUMNS, TABLE_CONSTRAINTS, ...):
Why should two reads, three minutes apart, on INFORMATION_SCHEMA.COLUMNS make for two heavyweight operations?
Thankfully, that much is solved by using the notion of auto caching tables. We can cache the results of the first query over INFORMATION_SCHEMA.COLUMNS to have an immediate, cached result on our second access.
But this still leaves us with a problem: once in a while we will have to actually query INFORMATION_SCHEMA, since the cache is invalidated. Then what? Do we wait for the crash?
So the good news is that there is a way around this problem, too. We can query INFORMATION_SCHEMA in such way that it does not make for a crash risk. This is now implemented in common_schema's auto-caching tables. And here's how it works:
There are INFORMATION_SCHEMA optimizations. If you use, for example, the TABLES table and you query using a given constant TABLE_SCHEMA, as in:
SELECT * FROM INFORMATION_SCHEMA.TABLES WHERE TABLE_SCHEMA='sakila';
You're using an optimization which avoids even looking at databases which are not 'sakila'. If you further use the TABLE_NAME, as in:
SELECT * FROM INFORMATION_SCHEMA.TABLES WHERE TABLE_SCHEMA='sakila' AND TABLE_NAME='actor';
You're using another optimization: MySQL will not even scan the sakila directory, but will directly open the actor table.
If you only look for TABLE_NAME, the table won't even be opened. If only TABLE_TYPE is accessed, only the .frm file is opened (not table data).
The auto-caching solution involves executing a stored function which checks for invalidation and re-populates the table. Since this is a stored routine, we don't actually have to populate the cache table with a simple INSERT INTO ... SELECT. We could make it more complicated, but worthwhile.
This is the solution (illustrated against the TABLES table):
This makes the entire process longer than it could have been, working this one table at a time. But it's also safer: each time we only take locks for one table; releasing; acquire another; and so forth.
So, what does this mean?
It means you can auto-cache data from INFORMATION_SCHEMA, and not have to worry about it. You can define the invalidation method (e.g. 10 minutes cache). When invalidation does occur, you don't have to worry about it. Sure, you'll wait for the data, but you don't need to expect a crash.
common_schema comes with pre-defined autocahe tables for those heavyweight INFORMATION_SCHEMA. These are:
The names should be familiar; these are the same names as in INFORMATION_SCHEMA. Just point your queries to common_schema instead, and have everything else go as before. You will get long response time at first. But then, when data is cached, expect a great use of your metadata, like we all expect to. Oh, and your database doesn't crash while doing it.
Taking STATISTICS as an example, 1st SELECT COUNT(*) FROM STATISTICS returns with 0, because of MyISAM optimization. The next one returns with correct results, which are already cached. As a workaround, you may invoke SELECT SUM(1) FROM STATISTICS, which returns correct values at all times, including the first invocation.
The VIEWS tables poses for a slight problem: apparently autocache can't work on VIEWS, since it itself relies on views, and that makes for a weird infinite loop in MySQL. I'm not sure why, and have tried to overcome this, to date without success.


| Home | Libraries | People | FAQ | More |
This module provides adapters for boost::tuple. Including the module header makes boost::tuple a fully conforming Forward Sequence.
#include <boost/fusion/adapted/boost_tuple.hpp> #include <boost/fusion/include/boost_tuple.hpp>
boost::tuple<int,std::string> example_tuple(101, "hello"); std::cout << *boost::fusion::begin(example_tuple) << '\n'; std::cout << *boost::fusion::next(boost::fusion::begin(example_tuple)) << '\n';


/
/
AppTribution
{{ project_name }}
Feature ID: 22
Residential
School
Market/Prominent Stores
Agricultural & Agro Industrial
Medical Institutions
Barangay Hall
Military Institution
Sports Center/Gymnasium/Covered Court
Telecommunication Facilities
Transport Terminal (Road,Rail, Air, and Marine)
Warehouse
Powerplant/Substation
NGS/CSO Offices
Police Station
Water Supply/Storage
Religious Institutions
Bank
Factory
Gas Station
Fire Station
Other Government Offices
Other Commercial Establishments
Building Type
Building Name
save
nature_people
directions_run
star
Visible Features:
Marked Features:
View Radius