OptionAttribute is applied on the properties of the option contract class, it is for getting the value of the corresponding argument when the argument is parsed.
Consider the following code snippet that we have mentioned before:
|
[ Option( Name="/method;/m", Type=OptionType.SingleValue, Description="Specifies the calculation method\n"+ "\t\tadd: Performs an addition calculation\n"+ "\t\tsub: Performs a subtraction calculation\n"+ "\t\tmul: Performs a multiplication calculation\n"+ "\t\tdiv: Performs a division calculation", Required=true) ] public string Method { get; set; } |
The argument (option) here is a single value option, which means user should provide a "/method:value" or "/m:value" argument when using the command line. For ACF, it will use the value of the argument (value here) to populate the Method property.
There is a question here, what type should I take for the property when I apply the OptionAttribute on it? In the above example, it is obvious that we should use a string property for the single value option, because no matter what type the value is, it is better to be stored in the string field. Following table shows the relationship between the option type and its corresponding property type.
|
Option Type |
Property Type |
| SingleValue | System.String |
|
ValueList |
System.Array |
| Switch | System.Boolean |
Note: Arguments that are the type of Parameter are stored in a System.String field. For more information about Parameter type, please refer to the Parameter Type chapter.
Following is the detailed description and remarks of the properties in the OptionAttribute class.
Property Description Remarks
Name
Name of the option
It has the same meaning with the Argument property of the OptionContractAttribute. It defines the
name of the option. Use semicolon ";" to separate the different instance
of the names. For example: "/input;/i".
Description
Description of the option
This description text will be used for
generating the help information that will be shown on the help screen. It
is a best practice that specify the Description property for each option
in your contract.
Type
Type of the option
ACF supports two categories (four types) of
options. For more information about the option type please refer to Argument (Option)
Categories and Types chapter.
Required
Indicates if the option is required
(mandatory)
The default value is true, which means users
of the application must provide the option in the command line.
CaseSensitive
Specifies if the Option is case
sensitive
For example, if the CaseSensitive property is
set to true, "/input" and "/Input" are two different options.
ValueSeparator
Specifies the value separator character for
the values of an option
It just makes sense for ValueList options.
For ValueList options, users can use the separator character to separate
the values specified. For example, when this property is set to ",", users
must use the comma to separate the values in the ValueList option, like
"/inputs:a,b,c". When this property is set to ";", users
must use the semicolon to separate the values, like
"/inputs:a;b;c". The default value of this property is
',' (comma).
Default
Specifies the default value when the option
is not required (mandatory)
When the Required property is set to false,
Give the option a default value by using this
property.


Deep Belief Network (DBN) based on BiMDP¶
CodeSnippet
You can download all the code on this page from the code snippets directory
written by Pietro Berkes and Niko Wilbert
This is only a proof-of-concept, so don’t expect the DBN to actually work! Its current pupose is to experiment with different binet features.
Run this demo to view an HTML inspection of the DBN training. This should help with understanding how the implementation works.
DBN nodes — This is the original non-bimdp DBN node from Pietro, which is the basis for the BiMDP DBN implementation and is required for this example to run.
>>> import mdp
>>> import bimdp
>>> import dbn_binodes
create DBN
>>> n_layers = 2
>>> flow = dbn_binodes.get_DBN_flow(2, hidden_dims=[2,2])
create data
>>> n_samples = 10000 # number of data points
>>> n_greedy_reps = 100 # repetitions in greedy phase
>>> x = mdp.numx.zeros((n_samples, 4))
>>> for i in range(n_samples):
... r = mdp.numx.rand()
... if r>0.666:
... x[i,:] = [0.,1.,0.,1.]
... elif r>0.333:
... x[i,:] = [1.,0.,1.,0.]
n_layers iterables plus one iterable for the DBNMasterBiNode
>>> data_iterables = [None] + [[x] * n_greedy_reps] * n_layers + [[x]]
>>> msg_iterables = ([None] +
... [[{"epsilon": 0.1, "decay": 0.0,
... "momentum": 0.0}] * n_greedy_reps] * n_layers +
... [[{"top_updates": 3, "epsilon": 0.1, "decay": 0.0,
... "momentum": 0.0,
... "max_iter": 2, "min_error": -1.0}]])
perform and visualize the training
>>> bimdp.show_training(flow, data_iterables, msg_iterables, debug=True)
'/tmp/.../training_inspection.html'
>>> print "done."
done.
Other files:
- DBN nodes
- This is the original non-bimdp DBN node from Pietro, which is the basis for the BiMDP DBN implementation.
- DBN binodes
- The current BiMDP implementation of the DBN, based on the coroutine decorator for easy continuation.
- Old DBN coroutine binodes
- Older version based on a coroutine, but without using the coderoator. This might still be useful to understand how the codecorator actually works.
- Old DBN statemachine binodes
- Older version based on a state machine implementation.


The concept of many processor working together in concert in not new in the graphic field of the computer science. Since the demand generated by entertainment started to growth multi-core hardware emerged in order to take advantage of the high parallel task of generating 3D image. In computer graphics, the process of generating a 3D images consist of refreshing pixels at rate of sixty or more Hz. Each pixel to be processed goes through a number of stages, and this process is commonly referred to as the graphic processing pipeline. The peculiarity of this task is that the computation each pixel is independent of the other’s so this work is perfectly suitable for distribution over parallel processing elements. To support extremely fast processing of large graphics data sets (vertices and fragments), modern GPUs employ a stream processing model with parallelism. The game industry boosted the development of the GPU, that offer now greater performance than CPUs and are improving faster too (see Figure [CPU-VS-GPU_GFLOP] and [CPU-VS-GPU_MEMORY]). The reason behind the discrepancy in floating-point capability between CPU and GPU is that GPU is designed such that more transistors are devoted to data processing rather than caching and flow control.
The today’s Top 500 Supercomputers10 ranking is dominated by massively parallel computer, built on top of superfast networks and millions of sequential CPUs working in concert but as the industry is developing even more powerful, programmable and capable GPUs in term of GFlops we see that they begin to offer advantages over traditional cluster of computers in terms of economicity and scalability.


Usage Type:
- Type: {{affiliateform.type | enum:'affiliate.type.'}}
- Subtype: {{affiliateform.subType | enum:'affiliate.subType.'}} {{affiliateform.otherText}}
- Agreement Type: {{affiliateform.affiliateDetails.agreementType | enum:'affiliate.agreementType.'}}
Contact Information:
- Name: {{affiliateform.affiliateDetails.firstName}} {{affiliateform.affiliateDetails.lastName}}
- Email: {{affiliateform.affiliateDetails.email}}
- Landline Phone Number: {{affiliateform.affiliateDetails.landlineNumber}} ext: {{affiliateform.affiliateDetails.landlineExtension}}
- Mobile Phone Number: {{affiliateform.affiliateDetails.mobileNumber}}
- Address: {{affiliateform.affiliateDetails.address.street}}
- City: {{affiliateform.affiliateDetails.address.city}}
- Post Code: {{affiliateform.affiliateDetails.address.post}}
- Country: {{affiliateform.affiliateDetails.address.country.commonName}}
Billing Address:
- Address: {{affiliateform.affiliateDetails.billingAddress.street}}
- City: {{affiliateform.affiliateDetails.billingAddress.city}}
- Post Code: {{affiliateform.affiliateDetails.billingAddress.post}}
- Country: {{affiliateform.affiliateDetails.billingAddress.country.commonName}}
Organization:
- Organization Name: {{affiliateform.affiliateDetails.organizationName}}
- Organization Type:
{{affiliateform.affiliateDetails.organizationTypeOther}}
{{affiliateform.affiliateDetails.organizationType | enum:'affiliate.organizationType.'}}
- Address: {{affiliateform.affiliateDetails.address.street}}
- City: {{affiliateform.affiliateDetails.address.city}}
- Post Code: {{affiliateform.affiliateDetails.address.post}}
- Country: {{affiliateform.affiliateDetails.address.country.commonName}}
- Phone Number: {{affiliateform.affiliateDetails.landlineNumber}} ext: {{affiliateform.affiliateDetails.landlineExtension}}
- Alternate Email: {{affiliateform.affiliateDetails.alternateEmail}}
- Third Email: {{affiliateform.affiliateDetails.thirdEmail}}
- Other:{{otherText}}
Billing Address:
- Address: {{affiliateform.affiliateDetails.billingAddress.street}}
- City: {{affiliateform.affiliateDetails.billingAddress.city}}
- Post Code: {{affiliateform.affiliateDetails.billingAddress.post}}
- Country: {{affiliateform.affiliateDetails.billingAddress.country.commonName}}
Usage
- Current Usage: {{CommercialUsageService.currentCommercialUsageReport.context.currentUsage}}
- Planned Usage: {{CommercialUsageService.currentCommercialUsageReport.context.plannedUsage}}
- Purpose: {{CommercialUsageService.currentCommercialUsageReport.context.purpose}}
- Implementation Status: {{CommercialUsageService.currentCommercialUsageReport.context.implementationStatus | enum:'affiliate.implementationStatus.'}}
- Other Activities: {{CommercialUsageService.currentCommercialUsageReport.context.otherActivities}}
-
Country: {{'global.country.' + countryRecord.country.isoCode2 | translate}}
Practices: {{countryRecord.snomedPractices | number}}
Hospitals/Institutions:
{{entry.name}},
{{alert.msg}}


Classics
Panel content goes here
CLICK ME
The Flower Series
Panel content goes here
Salt O' the Sea
Panel content goes here


Class Response
Represents a response returned from Instagram's API.
Namespace: Instagram\Http
Package: Instagram
License: MIT
Author: Hassan Khan<EMAIL_ADDRESS>Link: https://github.com/hassankhan/instagram-sdk
Located at Http/Response.php
Package: Instagram
License: MIT
Author: Hassan Khan<EMAIL_ADDRESS>Link: https://github.com/hassankhan/instagram-sdk
Located at Http/Response.php
public
|
#
__construct( array $meta = [], array $data = [], array $pagination = [] )
Creates a new instance of |
public static
static
|
#
createFromJson( array $response )
Creates a new instance of |
public
array
|
|
public
array|Illuminate\Support\Collection
|
|
public
|
#
merge(
Merges the contents of this response with |
protected
boolean
|
#
isCollection( array|Illuminate\Support\Collection $data )
Tests the current response data to see if one or more records were returned. |
protected
boolean
|
|
public
boolean
|
|
public
string|null
|
|
public
string
|
protected
array
|
$raw
|
|
protected
array
|
$meta
|
|
protected
array
|
$data
|
|
protected
array
|
$pagination
|


All samples are using Ink.requireModules
, please read how to use it at Ink.requireModules section
| Function name | Description |
|---|---|
| .get(format, [_date]) | Formats a date object. |
| .set([format], str_date) | Creates a date object based on a format string. |
Formats a date object.
This works exactly as php date() function. https://php.net/manual/en/function.date.php
format
The format in which the date it will be formatted._date
The date to format. Can receive unix timestamp or a date object. Defaults to current time.Creates a date object based on a format string.
This works exactly as php date() function. https://php.net/manual/en/function.date.php
format
The format in which the date will be formatted. Defaults to 'Y-m-d'str_date
The date formatted.


This package mainly contains a plotting function ggseg
and data.frames of different brain atlases for plotting. Plotting results of analyses on regions or networks often involves swapping between statistical tools, like R and Matlab, and software for brain imaging to correctly visualise analysis results.
This package aims to make it possible to plot results directly through R.
There are currently four atlases available in the package:
dkt
- Desikan-Killany atlas (aparc).aseg
- Automatic subcortical segmentation.We are working on creating a detailed description in the wiki on how to create and contribute atlases to the package. The ggseg
function already allows you to provide it with a data.frame of a custom atlas if you have it, but is must correspond to certain specifications to work.
Please see the wiki for information on adding atlases, or inspect the included datasets for requirements. If anything is unclear in the wiki, give us a shout out in the issues!
You may find more atlases in the companion package ggsegExtra.
The package can be installed using devtools. The package includes some large datasets for the brain coordinates. Please be patient during download and install, it will take some time.
The functions are now installed, and you may load them when you want to use them. All functions are documented in standard R fashion.
The package also has a vignette, to help you get started using it. You can access it here, or via R:
You can also see one of the creators blog for introductions to its use here
Don’t hesitate to ask for support using github issues, or requesting new atlases. While we would love getting help in creating new atlases, you may also request atlases through the issues, and we will try to get to it.


|
psl1ght
A free SDK for Sony's PS3 console
|
Process management. More...
#include <ppu-types.h>
Go to the source code of this file.
Functions | |
| void | sysProcessExit (int status) __attribute__((noreturn)) |
| Terminate the running process. More... | |
| void | sysProcessExitSpawn2 (const char *path, const char *argv[], const char *envp[], void *data, size_t size, int priority, u64 flags) |
| Terminate the running process and execute another program. More... | |
Process management.
| void sysProcessExit | ( | int | status | ) |
Terminate the running process.
This function does not return.
| status | Process return status. |
| void sysProcessExitSpawn2 | ( | const char * | path, |
| const char * | argv[], | ||
| const char * | envp[], | ||
| void * | data, | ||
| size_t | size, | ||
| int | priority, | ||
| u64 | flags | ||
| ) |
Terminate the running process and execute another program.
This function returns only in case of error.
| path | Path and name of new program to be executed. |
| argv | Commandline arguments for the new program. |
| envp | Environment variables for the new program (may be NULL ). |
| data | Additional data (may be NULL ). |
| size | Size of additional data. |
| priority | Priority. |
| flags | Process flags. It contains an OR'ed combination of the following values: |


iterami/Docs.htm/Repositories/MultiverseEditor.gtk
- Installation:
- "git clone --depth 1 https://github.com/iterami/MultiverseEditor.gtk.git"
- "sudo aptdcon -i libglew-dev libgtk-3-dev"
- "git clone --depth 1 https://github.com/iterami/common.git" into the same directory as "MultiverseEditor.gtk".
- Use your terminal to navigate to the "MultiverseEditor.gtk" directory and "make" it.
- Run the compiled "~/.iterami/multiverseeditor".
- Updating:
- Close "MultiverseEditor.gtk".
- "git pull" the "common" and "MultiverseEditor.gtk" repositories that you cloned.
- Use your terminal to navigate to the "MultiverseEditor.gtk" directory and "make" it.
- Run the compiled "~/.iterami/multiverseeditor".
- Uninstallation:
- Use your terminal to navigate to the "MultiverseEditor.gtk" directory.
- "make clean"
- Delete the cloned "MultiverseEditor.gtk" directory.
- Optionally remove "common", optionally "sudo aptdcon -i libglew-dev", and optionally "sudo aptdcon -i libgtk-3-dev".
- If you have no other iterami GTK repositories installed, you can delete the "~/.iterami/" directory.
| A
| Move Camera Left
|
| C
| Move Camera Down
|
| Ctrl + H
| Return to Origin
|
| Ctrl + Q
| Quit
|
| D
| Move Camera Right
|
| S
| Move Camera Back
|
| Space
| Move Camera Up
|
| W
| Move Camera Forward
|


After college, I roomed with a close friend of mine, really great guy and considerate roommate. We got along great, but we had one problem. Well, two problems actually. Cats. 2 cats to be precise. I get along fine with animals, but these cats were really not friendly. In fact, after living with him and the cats for a year, I never once pet the cats. I would, however, walk into my own bedroom after a long day at work only to be scared half to death by the hiss of a cat lying in my own bed.
Not to pile on the cats too much, but they were pretty terrible. They shed all over the house and tracked kitty litter everywhere. Every time you went in or out of the house you had to squeeze through the door lest they make a run for it. If they did make a run for it, you’d spend the next few hours trying to lure them back.
Well after about a year, I desperately wanted him to get rid of the cats, but didn’t want to provoke a conflict. Primarily, I was worried about how my roommate would respond. I knew he would get rid of the cats if I asked him, but he loved them so much and I knew he would feel invalidated if I asked him to get rid of them.
I find some people are much easier to express conflicts to. I have a lot of siblings, we are constantly in conflict in minor ways and constantly arguing through those conflicts. When I voice my opinion to a sibling, I can trust that the other party will voice their opinion openly and without reservation. With my roommate, I felt, rightly or wrongly, that I would express my opinion openly and without reservation, but that he would simply cave and avoid the conflict. Ironically, this kept me from addressing the conflict for months.
When I finally ask that he get rid of them, he acquiesced and was sad. I wish he had pushed back a little more because then we would have had a more productive conversation.
I generally feel very comfortable being in conflict, where I struggle the most is dealing with others who want to avoid it. I’ve worked with people who are conflict averse and think everything is going great, only to find out a year later that they had some issues that were never voiced. I’ve tried to address this issue by soliciting feedback and being more self aware, but really the best solution I’ve come up with is to acknowledge my shortcomings and work primarily with other people who are comfortable being in conflict.
I’m happy to say that I got married last spring and my wife is someone who is very comfortable expressing her opinion. As a result, we are able to disagree frequently and passionately, but harbor no ill will. That makes life a lot easier and more enjoyable.


class: center, middle, inverse, title-slide # Multiple linear regression + Model selection --- class: center, middle ## Data cleaning --- ## Load data ```r pp <- read_csv("../../data/paris-paintings.csv", na = c("n/a", "", "NA")) ``` --- ## Shape and material Collapse levels of `Shape` and `mat`erial variables with `forcats::fct_collapse`: .small[ ```r pp <- pp %>% mutate( Shape = fct_collapse(Shape, oval = c("oval", "ovale"), round = c("round", "ronde"), squ_rect = "squ_rect", other = c("octogon", "octagon", "miniature")), mat = fct_collapse(mat, metal = c("a", "br", "c"), canvas = c("co", "t", "ta"), paper = c("p", "ca"), wood = "b", other = c("e", "g", "h", "mi", "o", "pa", "v", "al", "ar", "m")) ) ``` ] --- ## Review fixes .pull-left[ ```r pp %>% count(Shape) ``` ``` ## # A tibble: 5 x 2 ## Shape n ## <fct> <int> ## 1 other 12 ## 2 oval 52 ## 3 round 74 ## 4 squ_rect 3219 ## 5 <NA> 36 ``` ] .pull-right[ ```r pp %>% count(mat) ``` ``` ## # A tibble: 6 x 2 ## mat n ## <fct> <int> ## 1 metal 321 ## 2 other 59 ## 3 wood 886 ## 4 paper 38 ## 5 canvas 1783 ## 6 <NA> 306 ``` ] --- ## Main effects, numerical predictors ```r m_main_n <- lm(log(price) ~ Width_in + Height_in, data = pp) tidy(m_main_n) %>% select(term, estimate) %>% mutate(estimate = round(estimate, 3)) ``` ``` ## # A tibble: 3 x 2 ## term estimate ## <chr> <dbl> ## 1 (Intercept) 4.77 ## 2 Width_in 0.027 ## 3 Height_in -0.013 ``` --- ## Visualizing the model
--- --- --- ## Price, surface area, and living artist - Explore the relationship between price of paintings and surface area, conditioned on whether or not the artist is still living - First visualize and explore, then model - But first, prep the data .midi[ ```r pp <- pp %>% mutate(artistliving = if_else(artistliving == 0, "Deceased", "Living")) pp %>% count(artistliving) ``` ``` ## # A tibble: 2 x 2 ## artistliving n ## <chr> <int> ## 1 Deceased 2937 ## 2 Living 456 ``` ] --- --- ## Typical surface area .question[ What is the typical surface area for paintings? ] ![](x4-mlr-model-select_files/figure-html/viz-surf-artistliving-1.png)<!-- --> -- Less than 1000 square inches (~ 80cm x 80cm). There are very few paintings that have surface area above 5000 square inches. --- ## Main effects, numerical and categorical predictors .small[ ```r pp_Surf_lt_5000 <- pp %>% filter(Surface < 5000) m_main <- lm(log(price) ~ Surface + factor(artistliving), data = pp_Surf_lt_5000) tidy(m_main) %>% select(term, estimate) %>% mutate(exp_estimate = round(exp(estimate), 4)) ``` ``` ## # A tibble: 3 x 3 ## term estimate exp_estimate ## <chr> <dbl> <dbl> ## 1 (Intercept) 4.88 132. ## 2 Surface 0.000265 1.00 ## 3 factor(artistliving)Living 0.137 1.15 ``` - Paintings that are by an artist who is not alive and that have a surface area of 0 square inches are predicted, on average, to be 131.6417 livres. - For each additional square inch in painting's surface area, the price of the painting is predicted, on average, to be higher by a factor of 1.0003. - Paintings by a living artist are predicted, on average, to be higher by a factor of 1.1471 compared to paintings by an artist who is no longer alive. ] --- ## Facet to get a better look ![](x4-mlr-model-select_files/figure-html/viz-surf-lt-5000-artistliving-facet-1.png)<!-- --> --- ## What went wrong? .question[ Why is our linear regression model (right) different from what we get from `geom_smooth(method = "lm")` (left)? ] .pull-left[ ![](x4-mlr-model-select_files/figure-html/unnamed-chunk-8-1.png)<!-- --> ] .pull-right[ ![](x4-mlr-model-select_files/figure-html/unnamed-chunk-9-1.png)<!-- --> ] --- ## What went wrong? (cont.) - The way we specified our model only lets `artistliving` affect the intercept. - Model implicitly assumes that paintings with living and deceased artists have the *same slope* and only allows for *different intercepts*. - What seems more appropriate in this case? * Same slope and same intercept for both colors * Same slope and different intercept for both colors * Different slope and different intercept for both colors? --- ## Interacting explanatory variables - Including an interaction effect in the model allows for different slopes, i.e. nonparallel lines. - This implies that the regression coefficient for an explanatory variable would change as another explanatory variable changes. - This can be accomplished by adding an interaction variable: the product of two explanatory variables. --- ## Interaction: surface * artist living .small[ ```r ggplot(data = pp_Surf_lt_5000, mapping = aes(y = log(price), x = Surface, color = factor(artistliving))) + geom_point(alpha = 0.3) + geom_smooth(method = "lm", formula = "y ~ x") + labs(x = "Surface", y = "Log(price)", color = "Living artist") ``` ![](x4-mlr-model-select_files/figure-html/unnamed-chunk-10-1.png)<!-- --> ] --- ## Modeling with interaction effects .small[ ```r m_int <- lm(log(price) ~ Surface + factor(artistliving) + Surface * factor(artistliving), data = pp_Surf_lt_5000) tidy(m_int) %>% select(term, estimate) ``` ``` ## # A tibble: 4 x 2 ## term estimate ## <chr> <dbl> ## 1 (Intercept) 4.91 ## 2 Surface 0.000206 ## 3 factor(artistliving)Living -0.126 ## 4 Surface:factor(artistliving)Living 0.000479 ``` ] $$ \widehat{log(price)} = 4.91 + 0.00021~surface - 0.126~artistliving $$ $$+ ~ 0.00048~surface \times artistliving $$ --- ## Interpretation of interaction effects - Rate of change in price as the surface area of the painting increases does vary between paintings by living and non-living artists (different slopes), - Some paintings by living artists are more expensive than paintings by non-living artists, and some are not (different intercept). .small[ .pull-left[ - Non-living artist: `\(\widehat{log(price)} = 4.91 + 0.00021~surface\)` `\(- 0.126 \times 0 + 0.00048~surface \times 0\)` `\(= 4.91 + 0.00021~surface\)` - Living artist: `\(\widehat{log(price)} = 4.91 + 0.00021~surface\)` `\(- 0.126 \times 1 + 0.00048~surface \times 1\)` `\(= 4.91 + 0.00021~surface\)` `\(- 0.126 + 0.00048~surface\)` `\(= 4.784 + 0.00069~surface\)` ] .pull-right[ ![](x4-mlr-model-select_files/figure-html/unnamed-chunk-12-1.png)<!-- --> ] ] --- ## Third order interactions - Can you? Yes - Should you? Probably not if you want to interpret these interactions in context of the data. --- class: center, middle # Quality of fit in MLR --- ## `\(R^2\)` - `\(R^2\)` is the percentage of variability in the response variable explained by the regression model. ```r glance(m_main)$r.squared ``` ``` ## [1] 0.01320884 ``` ```r glance(m_int)$r.squared ``` ``` ## [1] 0.0176922 ``` -- - Clearly the model with interactions has a higher `\(R^2\)`. -- - However using `\(R^2\)` for model selection in models with multiple explanatory variables is not a good idea as `\(R^2\)` increases when **any** variable is added to the model. --- ## `\(R^2\)` - first principles $$ R^2 = \frac{ SS\_{Reg} }{ SS\_{Total} } = 1 - \left( \frac{ SS\_{Error} }{ SS\_{Total} } \right) $$ .question[ `\(R^2\)` is calculated based on the output below. ] ```r anova(m_main) ``` ``` ## Analysis of Variance Table ## ## Response: log(price) ## Df Sum Sq Mean Sq F value Pr(>F) ## Surface 1 138.5 138.537 40.6741 2.058e-10 ## factor(artistliving) 1 6.8 6.810 1.9994 0.1575 ## Residuals 3188 10858.4 3.406 ``` --- ## Adjusted `\(R^2\)` $$ R^2\_{adj} = 1 - \left( \frac{ SS\_{Error} }{ SS\_{Total} } \times \frac{n - 1}{n - k - 1} \right), $$ where `\(n\)` is the number of cases and `\(k\)` is the number of predictors in the model -- - Adjusted `\(R^2\)` doesn't increase if the new variable does not provide any new informaton or is completely unrelated. -- - This makes adjusted `\(R^2\)` a preferable metric for model selection in multiple regression models. --- ## In pursuit of Occam's Razor - Occam's Razor states that among competing hypotheses that predict equally well, the one with the fewest assumptions should be selected. - Model selection follows this principle. - We only want to add another variable to the model if the addition of that variable brings something valuable in terms of predictive power to the model. - In other words, we prefer the simplest best model, i.e. **parsimonious** model. --- ## Comparing models It appears that adding the interaction actually increased adjusted `\(R^2\)`, so we should indeed use the model with the interactions. ```r glance(m_main)$adj.r.squared ``` ``` ## [1] 0.01258977 ``` ```r glance(m_int)$adj.r.squared ``` ``` ## [1] 0.01676753 ``` --- class: center, middle # Model selection --- ## Backwards elimination - Start with **full** model (including all candidate explanatory variables and all candidate interactions) - Remove one variable at a time, and select the model with the highest adjusted `\(R^2\)` - Continue until adjusted `\(R^2\)` does not increase --- ## Forward selection - Start with **empty** model - Add one variable (or interaction effect) at a time, and select the model with the highest adjusted `\(R^2\)` - Continue until adjusted `\(R^2\)` does not increase --- ## Model selection and interaction effects If an interaction is included in the model, the main effects of both of those variables must also be in the model If a main effect is not in the model, then its interaction should not be in the model. --- ## Other model selection criteria - Adjusted `\(R^2\)` is one model selection criterion - There are others out there (many many others!), we'll discuss some later in the course, and some you might see in another courses ---


Uniformization Tool for
Directional Statistics
Directional Dataset
Clear points
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input_output module¶
-
class
input_output.
input_data
(filename, filedir)¶ Bases:
object
Data object that handles reading in the data.
- Parameters
filename (string) – The filename of the input file.
filedir (string) – The directory containing the input file.
-
check_read
()¶ Method that determines if numpy array or pandas dataframe are to be read, based on the input file name.
- Returns
True if pandas dataframe is to be read; false if numpy array is to be read.
- Return type
bool
-
read_in
()¶ Reads in the data into an numpy array or pandas dataframe.
- Returns
Returns the data either in an array or dataframe.
- Return type
numpy array/pandas dataframe
-
class
input_output.
output_data
(data, indices, outdir, option, data2=0)¶ Bases:
object
Data object that handles plotting and writing data.
- Parameters
data (numpy array/pandas dataframe) – The data to be plotted/printed.
indices (integer list) – The column indices that remain if insignificant entries were skipped.
option (string) – The option that handles the output processing - choose from: expecval, MOpop, transdipmom, efield, aucofu.
data2 (numpy array/pandas dataframe) – The additional data, if two sets of data are processed for that specific output option.
-
plot0
()¶ Expectation value output method. Generates plot - the specified output data is generated and saved to the corresponding file.
-
plot1
()¶ MO populations output method. Generates data files and plot - the specified output data is generated and saved to the corresponding files.
-
plot2
()¶ Transition dipole moment output method. Generates data files and plot - the specified output data is generated and saved to the corresponding files.
-
plot3
()¶ Electric field output method. Generates data files and plot - the specified output data is generated and saved to the corresponding files.
-
plot4
()¶ Autocorrelation function output method. Generates data files and plot - the specified output data is generated and saved to the corresponding files.
-
plot5
()¶ Default output method - no output.
-
plotparams
(myplot, strx, stry)¶ Method to define plotting parameters such as font size.
- Parameters
myplot (string) – Name of the figure object (typically ‘ax’).
strx (string) – Name of the x axis label.
stry (string) – Name of the y axis label.
main module¶
-
main.
get_run_type
(name)¶ Determines the run type of the data processing.
- Parameters
name (string) – Determines a switch case based on the input file name. Possible options are expect.t, npop.t, table.dat, efield.t, nstate_i.t.
- Returns
The name of the function to call for the selected case.
- Return type
function name
-
main.
main
(data_in, dir_in, dir_out, threshd=1e-05)¶ Main function call if analysis package is to be run as a script.
- Parameters
data_in (string) – Input file name.
dir_in (string) – Input file directory.
dir_out (string) – Output file directory.
threshd (float) – The variance threshold below which data is considered as constant and not included in the output.
-
main.
run_abort
(data, threshd)¶
-
main.
run_efield
(data, threshd, dir_out)¶ Handles the call to read efield.t data; Fourier-transforms relevant values (values that are not constant) and plots the resulting spectrum.
- Parameters
data (numpy array) – The data to process.
threshd (float) – The variance threshold below which data is considered as constant and not included in the output.
dir_out (string) – Output file directory.
-
main.
run_expec
(data, threshd, dir_out)¶ Handles the call to read and plot expect.t data; only plots relevant values (values that are not constant).
- Parameters
data (pandas dataframe) – The data to process.
threshd (float) – The variance threshold below which data is considered as constant and not included in the output.
dir_out (string) – Output file directory.
-
main.
run_npop
(data, threshd, dir_out)¶ Handles the call to read npop.t data; discards irrelevant columns (columns that remain constant); constructs the correlation matrix and prints/plots the result.
- Parameters
data (pandas dataframe) – The data to process.
threshd (float) – The variance threshold below which data is considered as constant and not included in the output.
dir_out (string) – Output file directory.
-
main.
run_nstate
(data, threshd, dir_out)¶ Handles the call to read nstate_i.t data; calculates, prints and plots the autocorrelation function; Fourier-transforms and plots the autocorrelation function.
- Parameters
data (numpy array) – The data to process.
threshd (float) – The variance threshold below which data is considered as constant and not included in the output.
dir_out (string) – Output file directory.
-
main.
run_table
(data, threshd, dir_out)¶ Handles the call to read table.dat data; calculates Euclidean distance (L2 norm) of the vectors in the table.
- Parameters
data (numpy array) – The data to process.
threshd (float) – The variance threshold below which data is considered as constant and not included in the output.
dir_out (string) – Output file directory.
numerical module¶
-
numerical.
DFT
(wavef, realdft=True)¶ Function to compute the discrete Fourier transform of the given function.
- Parameters
wavef (numpy array, real or complex) – The data with time data in the first row and the real or complex-valued vectors in the following rows.
realdft (bool) – Denotes if only positive frequency components of the DFT are returned.
- Returns
The energy grid points of the Fourier-transformed function and the Fourier-transformed function.
- Return type
numpy array, real; numpy array, complex
-
numerical.
aucofu
(wavef)¶ Function to compute the autocorrelation function from the given vectors (with respect to the first time step).
- Parameters
wavef (numpy array, complex) – The wave function over time.
- Returns
The autocorrelation function over time.
- Return type
numpy array, complex
-
numerical.
calc_auto
(wavef)¶ Helper function to compute the vector overlap.
- Parameters
wavef (numpy array, complex) – The wave function over time.
- Returns
The autocorrelation function over time.
- Return type
numpy array, complex
statistical module¶
-
statistical.
check_significance
(data, threshd)¶ Checks which columns are significant based on the set threshold for numpy arrays and pandas dataframes. Deletes insignificant columns.
- Parameters
data (numpy array/pandas dataframe) – The data object.
threshd (float) – The variance threshold below which data is considered as constant and not included in the output.
- Returns
The data object without insignificant columns and the indices that correspond to the original columns that remain, for plotting/labeling purposes. purposes.
- Return type
numpy array/pandas dataframe, integer list/string list
-
statistical.
correlation_matrix
(data)¶ Calculates the correlation matrix of a dataframe and orders by degree of correlation.
- Parameters
data (pandas dataframe) – The data object.
- Returns
The correlation matrix sorted with highest correlation on the top.
- Return type
pandas series
-
statistical.
euclidean_distance
(list_ref, list_comp, vectors)¶ Calculates the Euclidean distance (L2 norm) between pairs of vectors.
- Parameters
list_ref (integer list) – A list with the indices of the reference vectors.
list_comp (integer list) – A list with the indices of the vectors to compare to.
data (numpy array) – The data object.
- Returns
The Euclidean distance (L2 norm) for comparison vs. reference vectors.
- Return type
numpy array


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| lavResiduals {lavaan} | R Documentation |
Residuals
Description
‘lavResiduals’ provides model residuals and standardized residuals from
a fitted lavaan object, as well as various summaries of these residuals.
The ‘residuals()’ (and ‘resid()’) methods are just shortcuts to
this function with a limited set of arguments.
Usage
lavResiduals(object, type = "cor.bentler", custom.rmr = NULL,
se = FALSE, zstat = TRUE, summary = TRUE, h1.acov = "unstructured",
add.type = TRUE, add.labels = TRUE, add.class = TRUE,
drop.list.single.group = TRUE,
maximum.number = length(res.vech), output = "list")
Arguments
object |
An object of class lavaan .
|
type |
Character.
If type = "raw" , this function returns the raw (= unscaled)
difference between the observed and the expected (model-implied) summary
statistics, as well as the standardized version of these residualds.
If type = "cor" , or type = "cor.bollen" , the observed and model
implied covariance matrices are first transformed to a correlation matrix
(using cov2cor() ), before the residuals are computed.
If type = "cor.bentler" , both the observed and model implied covariance
matrices are rescaled by dividing the elements by the square roots of the
corresponding variances of the observed covariance matrix.
|
custom.rmr |
list . Not used yet.
|
se |
Logical. If TRUE , show the estimated standard errors
for the residuals.
|
zstat |
Logical. If TRUE , show the standardized residuals, which
are the raw residuals divided by the corresponding (estimated) standard
errors.
|
summary |
Logical. If TRUE , show various summaries of the
(possibly scaled) residuals. When type = "raw" , we compute the
RMR. When type = "cor.bentler" ,
we compute the SRMR. When type = "cor.bollen" , we compute the CRMR.
An unbiased version of these summaries is also computed, as well as a
standard error, a z-statistic and a p-value for the test of exact fit
based on these summaries.
|
h1.acov |
Character. If "unstructured" , the observed summary
statistics are used as consistent estimates of the corresponding (unrestricted)
population statistics. If "structured" , the model-implied summary
statistics are used as consistent estimates of the corresponding (unrestricted)
population statistics. This affects the way the asymptotic variance matrix
of the summary statistics is computed.
|
add.type |
Logical. If TRUE , show the type of residuals
in the output.
|
add.labels |
If TRUE , variable names are added to the vectors
and/or matrices.
|
add.class |
If TRUE , vectors are given the ‘lavaan.vector’
class; matrices are given the ‘lavaan.matrix’ class, and symmetric
matrices are given the ‘lavaan.matrix.symmetric’ class.
This only affects the way they are printed on the screen.
|
drop.list.single.group |
If FALSE , the results are returned as
a list, where each element corresponds to a group (even if there is only
a single group). If TRUE , the list will be unlisted if there is
only a single group.
|
maximum.number |
Integer. Only used if output ="table" . Show only
the first maximum.number rows of the data.frame.
|
output |
Character. By default, output = "list" , and the output
is a list of elements. If output = "table" , only the residuals of
the variance-covariance matrix are shown in a data.frame, sorted from high
(in absolute value) to low.
|
Value
If drop.list.single.group = TRUE
, a list of (residualized) summary
statistics, including type, standardized residuals, and summaries. If
drop.list.single.group = FALSE
, the list of summary statistics is nested
within a list for each group.
References
Bentler, P.M. and Dijkstra, T. (1985). Efficient estimation via linearization
in structural models. In Krishnaiah, P.R. (Ed.),
Multivariate analysis - VI, (pp. 9–42). New York, NY: Elsevier.
Ogasawara, H. (2001). Standard errors of fit indices using residuals in
structural equation modeling. Psychometrika, 66(3), 421–436.
doi:10.1007/BF02294443
Maydeu-Olivares, A. (2017). Assessing the size of model misfit in structural
equation models. Psychometrika, 82(3), 533–558.
doi:10.1007/s11336-016-9552-7
Standardized Residuals in Mplus. Document retrieved from URL
http://www.statmodel.com/download/StandardizedResiduals.pdf
Examples
HS.model <- ' visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9 '
fit <- cfa(HS.model, data = HolzingerSwineford1939)
lavResiduals(fit)
[Package
lavaan version 0.6-8
Index]


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import {MatListModule} from '@angular/material/list';
MatListOption
Selector: mat-list-option
Exported as: matListOption| Name | Description |
|---|---|
@Input()
|
Theme color of the list option. This sets the color of the checkbox/radio. |
@Input()
|
Whether ripples for list items are disabled. |
@Input()
|
Whether the list-item is disabled. |
@Input()
|
The number of lines this list item should reserve space for. If not specified, lines are inferred based on the projected content. Explicitly specifying the number of lines is useful if you want to acquire additional space and enable the wrapping of text. The unscoped text content of a list item will always be able to take up the remaining space of the item, unless it represents the title. A maximum of three lines is supported as per the Material Design specification. |
@Input()
|
Whether the option is selected. |
@Input()
|
Whether the label should appear before or after the checkbox/radio. Defaults to 'after' |
@Input()
|
Value of the option |
@Input()
Deprecated
|
Whether the label should appear before or after the checkbox/radio. Defaults to 'after' |
| focus | |
|---|---|
|
Allows for programmatic focusing of the option. |
| getLabel | |
|---|---|
|
Gets the text label of the list option. Used for the typeahead functionality in the list. |
| toggle | |
|---|---|
|
Toggles the selection state of the option. |
MatActionList
Selector: mat-action-list
Exported as: matActionList| Name | Description |
|---|---|
@Input()
|
Whether ripples for all list items is disabled. |
@Input()
|
Whether the entire list is disabled. When disabled, the list itself and each of its list items are disabled. |
MatList
Selector: mat-list
Exported as: matList| Name | Description |
|---|---|
@Input()
|
Whether ripples for all list items is disabled. |
@Input()
|
Whether the entire list is disabled. When disabled, the list itself and each of its list items are disabled. |
MatListItem
Selector: mat-list-item a[mat-list-item] button[mat-list-item]
Exported as: matListItem| Name | Description |
|---|---|
@Input()
|
Indicates whether an item in a |
@Input()
|
Whether ripples for list items are disabled. |
@Input()
|
Whether the list-item is disabled. |
@Input()
|
The number of lines this list item should reserve space for. If not specified, lines are inferred based on the projected content. Explicitly specifying the number of lines is useful if you want to acquire additional space and enable the wrapping of text. The unscoped text content of a list item will always be able to take up the remaining space of the item, unless it represents the title. A maximum of three lines is supported as per the Material Design specification. |
MatNavList
Selector: mat-nav-list
Exported as: matNavList| Name | Description |
|---|---|
@Input()
|
Whether ripples for all list items is disabled. |
@Input()
|
Whether the entire list is disabled. When disabled, the list itself and each of its list items are disabled. |
MatSelectionList
Selector: mat-selection-list
Exported as: matSelectionList| Name | Description |
|---|---|
@Input()
|
Theme color of the selection list. This sets the checkbox color for all list options. |
@Input()
|
Function used for comparing an option against the selected value when determining which options should appear as selected. The first argument is the value of an options. The second one is a value from the selected value. A boolean must be returned. |
@Input()
|
Whether ripples for all list items is disabled. |
@Input()
|
Whether the entire selection list is disabled. When true, each list item is also disabled and each list item is removed from the tab order (has tabindex="-1"). |
@Input()
|
Whether radio indicator for all list items is hidden. |
@Input()
|
Whether selection is limited to one or multiple items (default multiple). |
@Output()
|
Emits a change event whenever the selected state of an option changes. |
|
The option components contained within this selection-list. |
|
The currently selected options. |
| deselectAll | |
|---|---|
|
Deselects all of the options. Returns the options that changed as a result. |
|
| Returns | |
MatListOption[]
|
|
| focus | |
|---|---|
|
Focuses the selection list. |
|
| Parameters | |
|
options? FocusOptions
|
|
| selectAll | |
|---|---|
|
Selects all of the options. Returns the options that changed as a result. |
|
| Returns | |
MatListOption[]
|
|
MatListItemTitle
Directive capturing the title of a list item. A list item usually consists of a title and optional secondary or tertiary lines.
Text content for the title never wraps. There can only be a single title per list item.
Selector: [matListItemTitle]
MatListItemLine
Directive capturing a line in a list item. A list item usually consists of a title and optional secondary or tertiary lines.
Text content inside a line never wraps. There can be at maximum two lines per list item.
Selector: [matListItemLine]
MatListItemMeta
Directive matching an optional meta section for list items.
List items can reserve space at the end of an item to display a control, button or additional text content.
Selector: [matListItemMeta]
MatListItemAvatar
Directive matching an optional avatar within a list item.
List items can reserve space at the beginning of an item to display an avatar.
Selector: [matListItemAvatar]
MatListItemIcon
Directive matching an optional icon within a list item.
List items can reserve space at the beginning of an item to display an icon.
Selector: [matListItemIcon]
MatSelectionListChange
Change event that is being fired whenever the selected state of an option changes.
| Name | Description |
|---|---|
|
Reference to the options that have been changed. |
|
Reference to the selection list that emitted the event. |
MatListConfig
Object that can be used to configure the default options for the list module.
| Name | Description |
|---|---|
|
Wheter icon indicators should be hidden for single-selection. |
MatListOptionTogglePosition
Type describing possible positions of a checkbox or radio in a list option with respect to the list item's text.
type MatListOptionTogglePosition = 'before' | 'after';
MAT_LIST
Injection token that can be used to inject instances of MatList
. It serves as
alternative token to the actual MatList
class which could cause unnecessary
retention of the class and its component metadata.
const MAT_LIST: InjectionToken<MatList>;
MAT_NAV_LIST
Injection token that can be used to inject instances of MatNavList
. It serves as
alternative token to the actual MatNavList
class which could cause unnecessary
retention of the class and its component metadata.
const MAT_NAV_LIST: InjectionToken<MatNavList>;
MAT_SELECTION_LIST_VALUE_ACCESSOR
const MAT_SELECTION_LIST_VALUE_ACCESSOR: any;
MAT_LIST_CONFIG
Injection token that can be used to provide the default options the list module.
const MAT_LIST_CONFIG: InjectionToken<MatListConfig>;


| null.space.dimension {mgcv} | R Documentation |
The thin plate spline penalties give zero penalty to some
functions. The space of these functions is spanned by a set of
polynomial terms. null.space.dimension
finds the dimension of this space, M, given
the number of covariates that the smoother is a function of, d,
and the order of the smoothing penalty, m. If m does not
satisfy 2m>d then the smallest possible dimension
for the null space is found given d and the requirement that
the smooth should be visually smooth.
null.space.dimension(d,m)
d |
is a positive integer - the number of variables of which the t.p.s. is a function. |
m |
a non-negative integer giving the order of the penalty functional, or signalling that the default order should be used. |
Thin plate splines are only visually smooth if the order of the wiggliness penalty, m, satisfies 2m > d+1. If 2m<d+1 then this routine finds the smallest m giving visual smoothness for the given d, otherwise the supplied m is used. The null space dimension is given by:
M=(m+d-1)!/(d!(m-1)!
which is the value returned.
An integer (array), the null space dimension M.
Simon N. Wood<EMAIL_ADDRESS>Wood, S.N. (2003) Thin plate regression splines. J.R.Statist.Soc.B 65(1):95-114
http://www.maths.bris.ac.uk/~sw15190/
require(mgcv) null.space.dimension(2,0)


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Azerbaijan v Wales kick-off time, TV channel and team news
Here's everything you need to know about the Euro 2020 qualifier in Baku
by Ian MitchelmoreWales take on Azerbaijan in the penultimate fixture of their Euro 2020 qualifying campaign this weekend.
Here's everything you need to know about the game in Baku.
What time is kick-off?
The match at the Bakcell Arena gets underway at 5pm on Saturday, November 16.
Is it on TV?
Yes, the match will be shown live on S4C and Sky Sports.
Coverage on S4C begins at 4:30pm while Sky Sports' Football and Main Event channels will have the buildup from 4:15pm.
You can follow updates on our live match blog on WalesOnline's Football page from 2pm.
What is the latest team news?
Left-back Neil Taylor and goalkeeper Danny Ward both miss out on the game due to personal reasons.
Newport County's Tom King has been called up in place of Leicester City goalkeeper Ward.
Tom Lawrence also misses out due to illness.
Elsewhere, Joe Rodon, Jonny Williams, Ben Woodburn and David Brooks missed out on the squad due to injury.
Aaron Ramsey and Gareth Bale are both available for selection.
What about suspensions?
Central midfielder Joe Allen is unavailable for the game in Baku.
He picked up his third booking of the qualifying campaign in the 1-1 draw with Croatia last time out meaning he must serve a one-game ban.
Daniel James and Gareth Bale are both one caution away from a suspension, meaning they will miss Tuesday's clash with Hungary at Cardiff City Stadium if they are shown a yellow card against Azerbaijan.
What has Ryan Giggs said?
"It's an important game tomorrow night but of course we will probably need to win both games," said the Wales boss.
"Tomorrow is a big game but also with a little eye on Tuesday aswell.
"They're two big games. Obviously you're running out of games. We're just focused on tomorrow and winning the game. It's as simple as that.
"The players are ready, they're looking forward to it. Azerbaijan had a good result at home last time against Croatia, so it's not easy, but the lads are ready for it."
What are the latest betting odds?
Azerbaijan win 6/1
Draw 16/5
Wales win 1/2
(Odds courtesy of The Pools, correct at time of publishing)
Who is the referee?
German referee Deniz Aytekin will take charge of proceedings in Baku.
He will be assisted by Eduard Beitinger, Rafael Foltyn and Christian Dingert.
What do Wales need to qualify for Euro 2020?
If Wales beat Azerbaijan, they will qualify automatically if they win against Hungary and Slovakia drop points in either of their remaining games against Croatia and Azerbaijan.
If Wales draw with Azerbaijan, they can still qualify automatically, but they'd need to beat Hungary (claiming a better head-to-head record against them in the process) and hope Slovakia claim no more than two points from their remaining games against Croatia and Azerbaijan.
Wales can't qualify automatically if they lose to Azerbaijan.
The Red Dragons will also miss out if Slovakia win their remaining two games.
Match facts
- Wales are unbeaten in seven meetings against Azerbaijan, winning six games and drawing once; they have faced no other side as many times without ever losing.
- This is the fourth time Azerbaijan will host Wales (P3 W0 D1 L2), losing 0-1 in the most recent such fixture in June 2009 due to a first half goal from David Edwards.
- Azerbaijan have won one of their last nine matches (W1 D2 L6), defeating Bahrain 3-2 in a friendly match in October. Their last competitive win came 364 days before this match against Faroe Islands in the UEFA Nations League (2-0).
- Wales have lost four of their 10 competitive games under Ryan Giggs (W4 D2 L4), three more losses than in their final 10 competitive matches under Chris Coleman (W4 D5 L1).
- Azerbaijan are currently winless in 11 European Championship qualifying matches, drawing four games and losing seven; their most recent such win was at home to Malta in March 2015 (2-0).
- Wales have earned eight points from their six European Championship qualifying matches during this campaign; during qualification for the 2016 edition of the competition, only two of the 25 sides to earn eight points or fewer after six matches eventually qualified for the main tournament (Russia and Turkey).
- Mahir Emreli and Ramil Seydaev have scored four of Azerbaijan’s five goals during this qualification stage, with Tamkin Xalilzade netting the other.
- Gareth Bale has scored seven goals and provided a further two assists in his 11 starts under Ryan Giggs – the Welshman scored in the reverse fixture back in September.
Wales squad
Hennessey, A Davies, King.
Gunter, A Williams, B Davies, C Roberts, Ampadu, Mepham, Lockyer, J Lawrence, Poole.
Ramsey, Allen, Wilson, Smith, Levitt, James, Vaulks, Morrell.
Bale, Vokes, T Roberts, Matondo, Moore.


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IN THE HIGH COURT OF JUDICATURE AT PATNA Criminal Miscellaneous No.56354 of 2017 Arising Out of PS.Case No. -64 Year- 2017 Thana -M ALSALAM I District- PATNA ====================================================== 1. Sanjay Singh @ Sanjay Kumar, son of Late Shivnath Prasad, Resident of Radha Market, Sharifaganj, Police Station- Malsalami, Patna city, Dist.- Patna. 2. Dhirendra Kumar, son of Late Shivnath Prasad, Resident of at Jafarabad, Raghavpur, Police Station- Malsalami, Dist.- Patna. .... .... Petitioner/s Versus 1. The State of Bihar. 2. Bihar State Pollution control Board, Beltron Bhawan, Shastri Nagar, Patna- 800023. .... .... Opposite Party/s ====================================================== Appearance : For the Petitioner/s : Mr. Digvijay Narayan Singh For the Opposite Party/s : Mr. Sri Amit Kumar Rakesh ====================================================== CORAM: HONOURABLE MR. JUSTICE SANJAY PRIYA ORAL ORDER
2 30-11-2017 Heard learned counsel for the petitioners and the State.
The petitioners apprehend arrest in Malsalami P.S. Case No. 64 of 2017 instituted for the offence under Sections 268, 270, 278, 308 and 326 of the Indian Penal Code.
Counsel for the petitioner has submitted that the meeting No. 104 of Bihar State Pollution Control Board was held on 19.09.2016. It was decided that after 31.08.2016 permission to run Bricks Kiln will be given, only to such Bricks Kiln, which will run through clear, technology, in five blocks of Patna namely Maner, Danapur, Phulwari Sharif, Patna Sadar and Fatuha Block. The notice was also published in newspapers and time was Patna High Court Cr.M isc. No.56354 of 2017 (2) dt.30-11-2017 2/3 provided to those Brick Kiln to convert them to cleaner Technology. In between 30.03.2017 to 02.04.2017 inspection was conducted and it was found that nineteen bricks kiln were running without following Rule 21 of Air (Prevention and Control of Pollution) Act, 1981 and also without adopting cleaner technology and therefore, the present case was lodged against the petitioners along with other bricks kiln agencies. The counsel for the petitioners has submitted that period for up-gradation of the bricks kiln has been extended to 31.08.2018 (Annexure P/2). The co- accused have been allowed anticipatory bail vide order dated 15.11.2017 by a co-ordinate Bench of this Court passed in Criminal Miscellaneous No. 51641 of 2017.
In such circumstances, prayer for anticipatory bail is allowed and it is ordered that the petitioners named above in the event of their arrest or surrender in the court below within six weeks from the date of receipt/production of copy of this order, shall be released on bail on furnishing bail bond of Rs. 10,000/- (ten thousand) each with two sureties of the like amount each in connection with Malsalami P.S. Case No. 64 of 2017 to the satisfaction of learned Judicial Magistrate, 1st Class, Patna City, subject to condition as laid down u/S 438(2) of the Cr.P.C. with further conditions (1) bailors should be local having sufficient Patna High Court Cr.M isc. No.56354 of 2017 (2) dt.30-11-2017 3/3 immovable property within the jurisdiction of the court concerned, (2) petitioners shall cooperate in the trial and shall be present on each and every date fixed by the court and absence on two consecutive dates without proper and reasonable reason, will automatically cancel bail bond of the petitioners and (3) if petitioners tamper with the evidence or the witnesses of the case, in that case, prosecution will be at liberty to move for cancellation of bail of the petitioners.
(Sanjay Priya, J) ajay gupta/-
U T


Notebooks
Story: !nanni's father's dress
!nanni's father's dress
Title
!nanni's father's dress
Collection
Summary
!nanni's father wore a back-dress of genet (or sua). He also wore a back-dress of weasel (or muishond, or dda). He wore a front-dress of ||ko. When he goes away he hangs up the back-dress.
Comments
1) This story is found in Book XI & XII-4
Contributors
Date
20 January 1880
Categories
Artefact and dress, History (personal)
Keywords
dress (worn by !nanni's father) , dress (worn by !kun men) , dress (the back-dress) , dress (the front-dress) , dress (made of weasel) , dress (made of genet) , dress (the skins used to make) , dress (actions performed with) , !nanni (what his father wore) , skins (used to make !kun back- and front-dress) , skins (of genet, or sua) , skins (of weasel, muishond or dda) , skins (of ||ko) , genet (and dress worn by !nanni's father) , genet (back-dress) , weasel (muishond or dda) , weasel (and dress worn by !nanni's father) , weasel (back-dress) , ||ko (and dress worn by !nanni's father) , ||ko (front-dress) , !kun (men, dress of) , men (!kun, their dress) , animals (skins of, worn by !kun men) , animals (!kun names of) , names (of animals, skins of which worn by !kun men)
Story Pages
9250-9251


Monospace 2011 Call for Speakers
Monospace is now accepting proposals for conference sessions. Proposals are due May 15, 2011 by 23:59 EST.
If you're a:
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Some specific areas we'd like to see represented are:
- Cross platform development with Linux, Mac, and Windows
- Cross platform mobile development with Android, iPhone, and Windows Phone 7
- Cross platform web development
- Cross platform embedded development
- Patterns and best practices for developing maintainable applications
- Making the most out of the new language features
- Time-saving developer productivity tips, tricks, and tools
- Using the cloud
Please take a look at our call for speakers for more details!
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I am programmer with a strong academic background and 8 years work experience of development and testing software.
Third Party Acquiring integration development.
C# (dotnet core), docker, octopus, Git, TeamCity.
Unit and integration testing.
Python Flask.
Heroku, Git.
Daily meeting with remote teams and clients.
C# (dotnet core) development, AWS (Lambda, Terraform, Neptune, S3, EC2, SQS, SNS, DynamoDb, Kinesis, IAM), Jenkins, Terraform, Git.
Reviewing code for quality and adherence to standards.
Deploy and configuration of web applications on different environments.
Pair programming
Development of fraud detection software: C# (Web, MVC, API, .NET Core 2.0), JavaScript, React Redux, Bootstrap, Microsoft Sql Server, Elasticsearch, TFS.
Blockchain development (Ethereum, Geth, Web3, Solidity).
Unit and integration testing.
Speaker on tech events.
Agile Development & Scrum.
Development of web sites of Bocconi University: C# (console and web applications, MVC, LINQ, Entity Framework), HTML5, JavaScript, AJAX, JQuery, CSS, SASS, Zurb Foundation, Oracle, GitHub, Sourcetree.
Peer code reviews. Participating in daily scrum meetings.


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