Severe Weather Events
NOAA Storm Dataset. USA 1950 - 2011
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Main Menu
- Instructions
Instructions:
-
How to Use the Information on this Disc
-
How to Return Information
-
How to File FAFSA or CSS/Profile


Provide a pattern-matching like syntax with functional fluent chainable api
Think pattern
as switch
keyword and match
as case
keyword but without need to break
the switch-case
expression
match
param should be a Predicate, if not create a predicate with strictEqualmatch
param should be an Evaluator, if not create an Evaluator with returnValueYou could set a a default evaluator (take pattern value as argument) return what you want
If a ProxyPatternMatching or PatternMatching is executed with no matching value. It will return the pattern value by default.
Use .default(evaluator)
for override
Generated using TypeDoc
Install
npm install @tpoisseau/pattern-matching npm install 'https://github.com/tpoisseau/js-pattern-matching#1.0.3'
Use
import pattern, {PatternMatching} from '@tpoisseau/pattern-matching'; import * as comparators from '@tpoisseau/pattern-matching/comparators'; import * as evaluators from '@tpoisseau/pattern-matching/evaluators';


--- layout: example categories: example/v1.0.0 version: v1.0.0 title: UTFGrid interactivity from multiple layers description: Pulling interactivity from overlapping layers made in TileMill tags: - layers - tilemill ---


Christchurch, New Zealand
Christchurch, New Zealand
Canterbury, New Zealand
Picton, New Zealand
Nelson, New Zealand
Paparoa National Park, New Zealand
Alex Knob, Franz Josef, New Zealand
Fox Glacier, New Zealand
Mt. Fox, New Zealand
Haast Pass, New Zealand
Wanaka, New Zealand
Wanaka, New Zealand
Roy's Peak, New Zealand
Otago, New Zealand
Queenstown Hill Summit Queenstown, New Zealand
Fiordland National Park, New Zealand
Milford Sound, New Zealand
Te Anau Downs, New Zealand
Lake Gunn, New Zealand
Te Anau, New Zealand
Ben Lomond, Queenstown, New Zealand
South Island, New Zealand
Lake Tekapo, New Zealand
Mount John University Observatory, New Zealand
Lake Tekapo, New Zealand
Geraldine, New Zealand
Sugarloaf, Christchurch, New Zealand
Jailhouse Accommodation, Christchurch, New Zealand
Tasman Sea
ACMI, Melbourne, Australia
Melbourne, Victoria, Australia
Geelong, Victoria, Australia
Coffs Harbour, New South Wales, Australia
North Lakes, Queensland, Australia
Brisbane, Queensland, Australia
Brisbane, Queensland, Australia
Brisbane, Queensland, Australia
Rainbow Bay/Gold Coast, Queensland, Australia
Byron Bay, Queensland, Australia
Coffs Harbour, New South Wales, Australia
Coffs Harbour, New South Wales, Australia
Port Macquarie, New South Wales, Australia
North Arm, New South Wales, Australia
Gosford, New South Wales, Australia
Singapore (about a year or two prior)
Testing Wacom + Krita
Uloola Falls, New South Wales, Australia
Art Gallery of New South Wales - ArtExpress 2021, New South Wales, Australia
Melbourne, Australia
Melbourne, Australia
Melbourne, Australia
Melbourne, Australia
Melbourne, Australia
Bass Strait, Australia
Tasmania, Australia
Hobart, Tasmania, Australia
Hobart, Tasmania, Australia
Mt Wellington, Tasmania, Australia
Launceston, Tasmania, Australia
Launceston, Tasmania, Australia
Tamar Island Wetlands Centre and Reserve, Tasmania, Australia
Adelaide, Australia
Adelaide Botanic Gardens, Australia
Adelaide, Australia
South Australia, Australia
Adelaide, Australia
Adelaide, Australia
Adelaide Parklands, Australia
Adelaide, Australia
South Australia
Hallett Cove, South Australia
Hallett Cove Conservation Park, South Australia
Art Gallery of South Australia, Adelaide
South Australia
Broken Hill, New South Wales, Australia
Unincorporated Far West Region, New South Wales, Australia
New South Wales, Australia
Dubbo, New South Wales, Australia
Central West XPT, New South Wales, Australia
Blue Mountains National Park, New South Wales, Australia
Eagle Rock and Curracurrong Falls, Royal National Park, New South Wales
Eagle Rock and Curracurrong Falls, Royal National Park, New South Wales
Wedding Cake Rock, New South Wales, Australia
Southern Tablelands, New South Wales, Australia
Canberra, Australia
Canberra, Australia
Canberra, Australia
Southern Highlands, New South Wales, Australia
Keith Longhurst Reserve, New South Wales, Australia
Minto, New South Wales, Australia
North Ryde, Sydney, Australia
Minto, New South Wales, Australia
Ultimo, Sydney, Australia
Minto, New South Wales, Australia


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Delaunay Refinement (DR):
Given a Delaunay triangulation in 2 dimensions, return a Delaunay triangulation
that includes all original points plus additional points such that no triangle
has an angle smaller than a threshold Theta.
Input and Output File Formats
Both the input and output need to be in the Triangles file format. The input
must be a valid Delaunay triangulation of its points. The output must
include all input points, plus any number of additional points, and
must be a valid Delaunay Triangulation of all its points.
Default Input Distributions
Each distribution should be run for
n=2,000,000.
For this benchmark the input triangulation is generated using a Delaunay
triangulation on various point distributions. These triangulations
are generated using the PBBS code for Delaunay triangulations, which also adds
in a small number of boundary points beyond the original input points.
The point sets are non degenerate.
-
(1) Points selected at random uniformly within a unit circle.
uniform -s -d 2 <n> <tmpname>
delaunay -o <filename> <tmpname>
-
(1) Points selected at random from the Kuzmin distribution. This is a 2d
version of the 3d Plummer distribution with points highly concentrated around the origin,
but some points far off.
plummer -d 2 <n> <tmpname>
delaunay -o <filename> <tmpname>


{% load xextends %} {% xextends "admin/base.html" with secondary_nav="tags" %} {% load datetime %} {% block admin_content %}
This tag hasn't been applied to any members.


| t01 |
‘, nature, —, generally, ii, publisher, chapter, evidence, question, view |
15 |
478 |
8 |
| t02 |
%, =, <, >, total, 1, 17, 11, 20, 22 |
15 |
175 |
29 |
| t03 |
research, review, advances, technologies, biology, development, recent, applications, approaches, technology |
15 |
1278 |
10 |
| t04 |
health, public, global, public_health, international, national, policy, surveillance, preparedness, security |
15 |
1367 |
85 |
| t05 |
respiratory, RSV, children, syncytial, respiratory_viruses, respiratory_syncytial_virus, rhinovirus, HRV, hbov, viruses |
15 |
1176 |
4 |
| t06 |
infection(s), respiratory, disease, viral, viral_infections, tract, common, acute, upper |
15 |
759 |
26 |
| t07 |
disease(s), infectious, infectious_disease(s), emerging, food, animal, pathogens, zoonotic |
15 |
903 |
10 |
| t08 |
model(s), epidemic, dynamics, mathematical, parameters, reproduction, estimates, spatial, stochastic |
15 |
965 |
256 |
| t09 |
IBV, strain(s), isolates, phylogenetic, bronchitis, infectious_bronchitis_virus, chicken(s), phylogenetic_analysis |
15 |
891 |
20 |
| t10 |
protein(s), binding, domain(s), residues, peptide, structure, amino, structural |
15 |
818 |
67 |
| t11 |
immune, immune_system, mechanisms, functions, autoimmune, response(s), homeostasis, innate, inflammatory |
15 |
788 |
3 |
| t12 |
replication, proteins, host, viral, cellular, RNA, viral_proteins, cycle, autophagy, viruses |
15 |
897 |
9 |
| t13 |
vaccine(s), vaccination, immunization, protection, immunogenicity, adjuvant, immunized, responses, mucosal |
15 |
872 |
7 |
| t14 |
cell(s), replication, vero, culture, lines, virus, EV71, in_vitro, vitro |
15 |
517 |
10 |
| t15 |
drug(s), antiviral, therapeutic, antiviral_drugs, treatment, inhibitors, approved, agents, therapies |
15 |
545 |
63 |
| t16 |
TB, countries, united, states, south, united_states, incidence, north, deaths, europe |
15 |
361 |
81 |
| t17 |
mice, macrophages, cytokine(s), lung, tnf-α, IL-6, inflammatory, LPS, ifn-γ |
15 |
599 |
11 |
| t18 |
COVID-19, sars-cov-2, 2020, 2019, wuhan, china, 2019-ncov, coronavirus, coronavirus_disease_2019, hubei |
15 |
573 |
536 |
| t19 |
RNA, frameshifting, reading, 3′, rnas, rdrp, ORF, 5′, frameshift, ribosomal |
15 |
713 |
12 |
| t20 |
care, emergency, ED, medical, hospital(s), staff, healthcare, department, management |
15 |
672 |
182 |
| t21 |
assay, detection, amplification, PCR, real-time, RT-LAMP, probes, RT-PCR, isothermal, primers |
15 |
725 |
46 |
| t22 |
day(s), group(s), inoculated, shedding, inoculation, weeks, rats, dpi |
15 |
331 |
0 |
| t23 |
antibodies, antibody, monoclonal, ELISA, sera, neutralizing, mab(s), monoclonal_antibodies, igg |
15 |
627 |
23 |
| t24 |
patients, admission, ICU, intensive, ARDS, ventilation, intensive_care_unit, hospital, stay, admitted |
15 |
373 |
91 |
| t25 |
prediction, data, algorithm(s), accuracy, method, learning, machine, software, database |
15 |
588 |
54 |
| t26 |
transmission, quarantine, contact(s), distancing, measures, social_distancing, cases, outbreak, tracing |
15 |
505 |
273 |
| t27 |
apoptosis, kinase, phosphorylation, cells, pathway, nf-κb, UPR, apoptotic, MAPK, activation |
15 |
600 |
3 |
| t28 |
diagnostic, test(s), testing, sensitivity, diagnosis, detection, laboratories, laboratory, assays |
15 |
442 |
46 |
| t29 |
entry, fusion, membrane, IFITM3, envelope, receptor, cholesterol, HIV-1, cell, env |
15 |
642 |
14 |
| t30 |
bat(s), species, hosts, humans, reservoir(s), mammals, zoonotic, niv |
15 |
621 |
26 |
| t31 |
cells, +, CD8, CD4, MHC, lymphocytes, t-cell, NK, CD8(+, t_cells |
15 |
584 |
12 |
| t32 |
CT, chest, imaging, KD, tomography, consolidation, diagnosis, cough, fever, pneumonia |
15 |
609 |
99 |
| t33 |
95, CI, =, odds, confidence, confidence_interval, regression, interval, risk, % |
15 |
210 |
29 |
| t34 |
IFN, innate, interferon(s), antiviral, ifn-β, ifns, signaling, type, response |
15 |
640 |
1 |
| t35 |
expression, gene(s), gene_expression, rnai, sirna(s), differentially, mirnas, lncrnas |
15 |
393 |
5 |
| t36 |
samples, swab(s), load, positive, viral_load, chain, specimens, nasal, PCR |
15 |
231 |
52 |
| t37 |
sequencing, genome(s), metagenomic(s), sequences, reads, NGS, virome, next-generation |
15 |
454 |
17 |
| t38 |
SARS, sars-cov, acute_respiratory_syndrome, syndrome, coronavirus, severe, acute, cov, 2003, respiratory |
15 |
165 |
42 |
| t39 |
epithelial, intestinal, epithelium, epithelial_cells, tissue(s), electron, mucosa, intestine, electron_microscopy |
15 |
340 |
5 |
| t40 |
influenza, H1N1, pandemic, H5N1, avian, HA, 2009, influenza_virus, H7N9, influenza_viruses |
15 |
290 |
0 |
| t41 |
RESULTS, BACKGROUND, METHODS, CONCLUSION(S), OBJECTIVE(S), FINDINGS, DESIGN, METHODOLOGY/PRINCIPAL |
None |
11 |
1 |
| t42 |
PEDV, porcine, pig(s), PRRSV, TGEV, piglets, swine, pdcov, diarrhea |
15 |
439 |
2 |
| t43 |
rotavirus, bovine, calves, cattle, diarrhea, herds, diarrhoea, coli, cryptosporidium, bcov |
15 |
421 |
0 |
| t44 |
students, respondents, survey, perceived, participants, questionnaire, nurses, perception(s), attitudes |
15 |
418 |
28 |
| t45 |
bacterial, pneumoniae, CAP, bacteria, antibiotic(s), streptococcus, aureus, antimicrobial, S. |
15 |
226 |
0 |
| t46 |
CNS, brain, nervous, MS, central_nervous_system, demyelination, astrocytes, neurons, sclerosis, spinal |
15 |
528 |
2 |
| t47 |
genetic, codon, polymorphism(s), usage, mutation(s), selection, snps, allele |
15 |
362 |
14 |
| t48 |
CRP, serum, count(s), patients, levels, c-reactive, blood, c-reactive_protein, PCT |
15 |
255 |
47 |
| t49 |
°, inactivation, water, purification, chromatography, ph, liquid, wastewater, MS2, filtration |
15 |
383 |
10 |
| t50 |
ILI, influenza, illness, influenza-like, surveillance, season, pertussis, influenza-like_illness, SARI, ARI |
15 |
274 |
2 |
| t51 |
placebo, trial, randomized, treatment, adverse_events, group, adverse, osteonecrosis, mg, femoral |
15 |
307 |
36 |
| t52 |
articles, published, literature, studies, publications, systematic, pubmed, searched, systematic_review, papers |
15 |
266 |
51 |
| t53 |
hcws, workers, mask(s), PPE, healthcare_workers, healthcare, N95, hygiene, hand |
15 |
375 |
50 |
| t54 |
activity, compound(s), derivatives, antiviral_activity, μm, inhibitory, IC50, IC(50, EC50 |
15 |
526 |
7 |
| t55 |
protease(s), catalytic, cleavage, 3clpro, enzymes, ubiquitin, substrate, proteinase, proteolytic |
15 |
467 |
9 |
| t56 |
dengue, ebola, ZIKV, zika, EBOV, DENV, west, CHIKV, EVD, fever |
15 |
207 |
0 |
| t57 |
mers-cov, middle, east, MERS, middle_east_respiratory_syndrome_coronavirus, saudi, camels, arabia, saudi_arabia, middle_east_respiratory_syndrome |
15 |
391 |
30 |
| t58 |
air, airborne, indoor, temperature, wind, aerosol(s), humidity, ventilation, bioaerosol |
15 |
362 |
24 |
| t59 |
calves, milk, fed, colostrum, feed, feeding, diet, intake, BW, kg |
15 |
255 |
0 |
| t60 |
plant(s), extract(s), medicinal, herbal, TCM, GA, metabolites, flavonoids |
15 |
381 |
5 |
| t61 |
tourism, travel, market(s), $, urban, economic, travelers, price, business |
15 |
352 |
9 |
| t62 |
MHV, sialic, lectin(s), glycosylation, glycans, carbohydrate, kda, glycoproteins, MHV-A59 |
15 |
336 |
0 |
| t63 |
recipients, transplant, transplantation, cancer, pregnant, pregnancy, women, pregnant_women, stem, HSCT |
15 |
244 |
8 |
| t64 |
nanoparticles, delivery, aptamer(s), biosensor(s), metal, nanomaterials, silver, nps |
15 |
384 |
0 |
| t65 |
asthma, exacerbation(s), COPD, kim, allergic, airway, lee, CF, atopic |
15 |
105 |
0 |
| t66 |
de, el, los, en, las, con, se, por, para, es |
15 |
203 |
26 |
| t67 |
les, de(s), à, une, dans, du, est, par, sont |
15 |
206 |
18 |
| t68 |
herpes, HSV-1, EBV, simplex, norovirus, FMDV, measles, HPV, HCMV, HSV |
15 |
100 |
0 |
| t69 |
cat(s), feline, FIP, FIPV, fcov, peritonitis, FIV, felv, infectious_peritonitis |
15 |
291 |
1 |
| t70 |
dog(s), canine, CPV, CDV, ccov, distemper, parvovirus, leishmania, CPV-2 |
15 |
246 |
1 |
| t71 |
ACE2, angiotensin, diabetes, ACE, angiotensin-converting, cardiovascular, RAS, heart, ang, renal |
15 |
241 |
14 |
| t72 |
mental, psychological, anxiety, depression, social, sleep, psychiatric, PTSD, mental_health, schizophrenia |
15 |
198 |
49 |
| t73 |
golgi, ER, und, endoplasmic, reticulum, der, channel, endoplasmic_reticulum, die, membrane |
15 |
245 |
1 |
| t74 |
-, authorized, SUPPLEMENTARY, supplementary, online_version, users, ELECTRONIC, version, MATERIAL, online |
15 |
29 |
0 |
| t75 |
HCV, HBV, HIV, hepatitis, HEV, hepatitis_c_virus, transfusion, liver, HIV-1, van |
15 |
73 |
0 |


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| 713 | (A) | the kynge and his concełłe carpis to-gedirs |
| 715 | (C) | The kynge & his counsełł . speken to-gedere |
| 707 | (D) | The kyng and his counceil carpyn to-gidir |
| 685 | (E) | ¶ The kyng & his cunseyl . carped to-gydre |
| (Ex) | ---- no text ---- | |
| 730 | (L) | þe kyng & his consail carpen to-gedre |
| 667 | (P) | þe kynge and his counseyle carpyd to-geder |
| 697 | (U) | The kyng and his counseil / carpen to-geder |
| (V) | ---- no text ---- |


class/1
declaration:- class
ClassName.
:- class
ClassName = [
SlotDef,
...].
:- class
ClassName =
Super.
:- class
ClassName = [
SlotDef,
...] +
Super +
....
:- class
ClassName = term(
Term).
:- class
ClassName = term(
Term,
Goal).
:- class
ClassName = term(
Term,
Goal,
Essence).
The definition of class ClassName begins
with this class/1
directive and ends with the next class/1
directive, the next end_class/[0,1]
directive, or the end of the file,
whichever comes first. All clauses that look like method
definitions within the scope of the class definition (that is,
which have one of <-/2
, <</2
or >>/2
as the principal
functors of their heads) are considered method definitions of
the class.
You may provide as many slot definitions (SlotDef) and superclasses (Super) as you like. All superclasses must be previously defined classes.
A slot definition (SlotDef) has the form
Visibility SlotName:Type = InitialValue
where Visibility and `= InitialValue' are optional.
Visibility is either public
, protected
, or
private
. If it is omitted, the slot is private.
SlotName must be an atom.
SlotType must be one of the following:
integer
integer_32
integer_16
integer_8
unsigned
unsigned_32
unsigned_16
unsigned_8
float
float_32
atom
address
term
pointer(
Type)
address
, except that access to this slot
yields, and update of this slot expects, a unary term whose functor
is Type
InitialValue may be any constant appropriate for the slot's type.
Term, if specified, is any compound Prolog term. Class declarations of any of the last three forms introduce a term class, which defines any term that unifies with Term as an instance of the class being defined.
Goal, if specified, is any Prolog goal. This goal may be used
to restrict which terms that unify with Term will be considered to
be instance of the class being defined. The default Goal is
true
. Other than when it is true
, Goal will usually share
variables with Term.
Essence, if specified, is a list of terms of the form
Variable:Type
where Variable is a variable apprearing somewhere in Term and
Type is one of the possible Slottype types listed above.
There should be a Variable:
Type pair for every variable in
Term. By specifying an essence, you permit much more space- and
time-efficient storage of and access to term slots.
Note that every class for which you want to be able to create instances must define at least one create method.
The following class definition is for a class
named point
, with two public slots, named x
and y
. Both
slots are of type integer
and have initial values of 1 and 2,
respectively.
:- class point = [public x:integer=1, public y:integer=2]. Self <- create. :- end_class point.
Because the slots are public, they have get and put methods generated automatically. Because the class has a create method defined, it is possible to create an instance with the command
| ?- create(point, PointObj).
which creates a point
object and binds the variable PointObj
to it.
Using the point
class, we could create a class, named_point
, which has
an extra public slot, name
.
:- class named_point = [public name:atom] + point. Self <- create(Name, X, Y) :- Self << name(Name), Self << x(X), Self << y(Y). :- end_class named_point.
The only way to create a named_point
object requires specifying values
for all three slots.
end_class/[0,1]


pangea.contrib.tagging package¶
Subpackages¶
Submodules¶
pangea.contrib.tagging.admin module¶
pangea.contrib.tagging.apps module¶
pangea.contrib.tagging.models module¶
-
class
pangea.contrib.tagging.models.
GenericTagRelationship
(*args, **kwargs)[source]¶ Bases:
pangea.core.mixins.AutoCreatedUpdatedMixin
-
payload
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
-
class
pangea.contrib.tagging.models.
SampleGroupTagRelationship
(id, created_at, updated_at, payload, tag, sample_group)[source]¶ Bases:
pangea.contrib.tagging.models.GenericTagRelationship
-
exception
DoesNotExist
¶ Bases:
django.core.exceptions.ObjectDoesNotExist
-
exception
MultipleObjectsReturned
¶ Bases:
django.core.exceptions.MultipleObjectsReturned
-
id
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
objects
= <django.db.models.manager.Manager object>¶
-
sample_group
¶ Accessor to the related object on the forward side of a many-to-one or one-to-one (via ForwardOneToOneDescriptor subclass) relation.
In the example:
class Child(Model): parent = ForeignKey(Parent, related_name='children')
Child.parent
is aForwardManyToOneDescriptor
instance.
-
sample_group_id
¶
-
tag
¶ Accessor to the related object on the forward side of a many-to-one or one-to-one (via ForwardOneToOneDescriptor subclass) relation.
In the example:
class Child(Model): parent = ForeignKey(Parent, related_name='children')
Child.parent
is aForwardManyToOneDescriptor
instance.
-
tag_id
¶
-
exception
-
class
pangea.contrib.tagging.models.
SampleTagRelationship
(id, created_at, updated_at, payload, tag, sample)[source]¶ Bases:
pangea.contrib.tagging.models.GenericTagRelationship
-
exception
DoesNotExist
¶ Bases:
django.core.exceptions.ObjectDoesNotExist
-
exception
MultipleObjectsReturned
¶ Bases:
django.core.exceptions.MultipleObjectsReturned
-
id
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
objects
= <django.db.models.manager.Manager object>¶
-
sample
¶ Accessor to the related object on the forward side of a many-to-one or one-to-one (via ForwardOneToOneDescriptor subclass) relation.
In the example:
class Child(Model): parent = ForeignKey(Parent, related_name='children')
Child.parent
is aForwardManyToOneDescriptor
instance.
-
sample_id
¶
-
tag
¶ Accessor to the related object on the forward side of a many-to-one or one-to-one (via ForwardOneToOneDescriptor subclass) relation.
In the example:
class Child(Model): parent = ForeignKey(Parent, related_name='children')
Child.parent
is aForwardManyToOneDescriptor
instance.
-
tag_id
¶
-
exception
-
class
pangea.contrib.tagging.models.
Tag
(*args, **kwargs)[source]¶ Bases:
pangea.core.mixins.AutoCreatedUpdatedMixin
This class represents the sample model.
-
exception
DoesNotExist
¶ Bases:
django.core.exceptions.ObjectDoesNotExist
-
exception
MultipleObjectsReturned
¶ Bases:
django.core.exceptions.MultipleObjectsReturned
Accessor to the related objects manager on the reverse side of a many-to-one relation.
In the example:
class Child(Model): parent = ForeignKey(Parent, related_name='children')
Parent.children
is aReverseManyToOneDescriptor
instance.Most of the implementation is delegated to a dynamically defined manager class built by
create_forward_many_to_many_manager()
defined below.
-
name
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
objects
= <django.db.models.manager.Manager object>¶
-
payload
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
Accessor to the related objects manager on the reverse side of a many-to-one relation.
In the example:
class Child(Model): parent = ForeignKey(Parent, related_name='children')
Parent.children
is aReverseManyToOneDescriptor
instance.Most of the implementation is delegated to a dynamically defined manager class built by
create_forward_many_to_many_manager()
defined below.
-
tagged_sample_groups
¶ Accessor to the related objects manager on the reverse side of a many-to-one relation.
In the example:
class Child(Model): parent = ForeignKey(Parent, related_name='children')
Parent.children
is aReverseManyToOneDescriptor
instance.Most of the implementation is delegated to a dynamically defined manager class built by
create_forward_many_to_many_manager()
defined below.
-
tagged_samples
¶ Accessor to the related objects manager on the reverse side of a many-to-one relation.
In the example:
class Child(Model): parent = ForeignKey(Parent, related_name='children')
Parent.children
is aReverseManyToOneDescriptor
instance.Most of the implementation is delegated to a dynamically defined manager class built by
create_forward_many_to_many_manager()
defined below.
-
uuid
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
exception
-
class
pangea.contrib.tagging.models.
TagTagRelationship
(id, created_at, updated_at, payload, tag, other_tag)[source]¶ Bases:
pangea.contrib.tagging.models.GenericTagRelationship
-
exception
DoesNotExist
¶ Bases:
django.core.exceptions.ObjectDoesNotExist
-
exception
MultipleObjectsReturned
¶ Bases:
django.core.exceptions.MultipleObjectsReturned
-
id
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
objects
= <django.db.models.manager.Manager object>¶
-
other_tag
¶ Accessor to the related object on the forward side of a many-to-one or one-to-one (via ForwardOneToOneDescriptor subclass) relation.
In the example:
class Child(Model): parent = ForeignKey(Parent, related_name='children')
Child.parent
is aForwardManyToOneDescriptor
instance.
-
other_tag_id
¶
-
tag
¶ Accessor to the related object on the forward side of a many-to-one or one-to-one (via ForwardOneToOneDescriptor subclass) relation.
In the example:
class Child(Model): parent = ForeignKey(Parent, related_name='children')
Child.parent
is aForwardManyToOneDescriptor
instance.
-
tag_id
¶
-
exception
pangea.contrib.tagging.serializers module¶
-
class
pangea.contrib.tagging.serializers.
TagAddSampleGroupSerializer
(instance=None, data=<class 'rest_framework.fields.empty'>, **kwargs)[source]¶ Bases:
rest_framework.serializers.Serializer
-
class
pangea.contrib.tagging.serializers.
TagAddSampleSerializer
(instance=None, data=<class 'rest_framework.fields.empty'>, **kwargs)[source]¶ Bases:
rest_framework.serializers.Serializer
-
class
pangea.contrib.tagging.serializers.
TagAddTagSerializer
(instance=None, data=<class 'rest_framework.fields.empty'>, **kwargs)[source]¶ Bases:
rest_framework.serializers.Serializer
-
class
pangea.contrib.tagging.serializers.
TagSerializer
(instance=None, data=<class 'rest_framework.fields.empty'>, **kwargs)[source]¶ Bases:
rest_framework.serializers.ModelSerializer
-
class
Meta
[source]¶ Bases:
object
-
fields
= ('uuid', 'name', 'created_at', 'updated_at', 'payload')¶
-
model
¶ alias of
pangea.contrib.tagging.models.Tag
-
-
class
pangea.contrib.tagging.tests module¶
Test suite for Sample model.
-
class
pangea.contrib.tagging.tests.
TagApiTests
(methodName='runTest')[source]¶ Bases:
rest_framework.test.APITestCase
pangea.contrib.tagging.urls module¶
-
pangea.contrib.tagging.urls.
path
(route, view, kwargs=None, name=None, *, Pattern=<class 'django.urls.resolvers.RoutePattern'>)¶
pangea.contrib.tagging.views module¶
-
class
pangea.contrib.tagging.views.
TagCreateView
(**kwargs)[source]¶ Bases:
pangea.core.views.utils.PermissionedListCreateAPIView
-
filterset_fields
= ['uuid', 'name']¶
-
permission_classes
= (<class 'rest_framework.permissions.IsAuthenticatedOrReadOnly'>,)¶
-
queryset
¶
-
serializer_class
¶
-
-
class
pangea.contrib.tagging.views.
TagDetailsView
(**kwargs)[source]¶ Bases:
rest_framework.generics.RetrieveUpdateDestroyAPIView
This class handles the http GET, PUT and DELETE requests.
-
permission_classes
= (<class 'rest_framework.permissions.IsAuthenticatedOrReadOnly'>,)¶
-
queryset
¶
-
serializer_class
¶
-
-
class
pangea.contrib.tagging.views.
TagSampleGroupsView
(**kwargs)[source]¶ Bases:
rest_framework.generics.ListAPIView
This class handles tagging sample groups.
-
filter_queryset
(queryset)[source]¶ Given a queryset, filter it with whichever filter backend is in use.
You are unlikely to want to override this method, although you may need to call it either from a list view, or from a custom get_object method if you want to apply the configured filtering backend to the default queryset.
-
get_serializer_class
()[source]¶ Return the class to use for the serializer. Defaults to using self.serializer_class.
You may want to override this if you need to provide different serializations depending on the incoming request.

TagSamplesView
(**kwargs)[source]¶ Bases:
rest_framework.generics.ListAPIView
This class handles tagging samples.
-
filter_queryset
(queryset)[source]¶ Given a queryset, filter it with whichever filter backend is in use.
You are unlikely to want to override this method, although you may need to call it either from a list view, or from a custom get_object method if you want to apply the configured filtering backend to the default queryset.
-
get_serializer_class
()[source]¶ Return the class to use for the serializer. Defaults to using self.serializer_class.
You may want to override this if you need to provide different serializations depending on the incoming request.

TagTagsView
(**kwargs)[source]¶ Bases:
rest_framework.generics.ListAPIView
This class handles managing membership of sample groups within projects.
-
get_serializer_class
()[source]¶ Return the class to use for the serializer. Defaults to using self.serializer_class.
You may want to override this if you need to provide different serializations depending on the incoming request.
(Eg. admins get full serialization, others get basic serialization)
-
permission_classes
= (<class 'rest_framework.permissions.IsAuthenticatedOrReadOnly'>,)¶
-
queryset
¶
-


CANID
— CAN identifier¶
-
class
CANID
(canid[, extended=False])¶ Represents a CAN identifier.
- Parameters
canid (int) – The CAN identifier of the frame
extended – Set to True if the ID is a 29-bit identifier.
- Raises
ValueError – if canid is not in the range 0..0x7ff (when extended is False) or 0..0x1fffffff (when extended is True).
A
CANID
instance is created either directly or is returned from theget_canid()
call.
Methods¶
-
is_extended
()¶ Indicates if the CAN ID is a standard 11-bit ID or an extended 29-bit ID.
- Returns
True if the CAN ID is a 29-bit identifier
- Return type
bool
-
get_id
()¶ Returns the integer value of the CAN ID
- Returns
the frame’s CAN ID represented as an integer
- Return type
int
-
get_id_filter
()¶ Returns a CAN ID filter that accepts this (and only this) ID
- Returns
the ID filter for the CAN ID
- Return type


THREE COLORS: RED A film review by Bryant Frazer Copyright 1994 Bryant Frazer
Directed by Krzysztof Kieslowski Written by Krzysztof Kieslowski and Krzysztof Piesiewicz
They don't make movies like this anymore. Even the most self-conscious of European films, destined to be widely promoted in America with dull trailers, tasteful posters, and an art house blitz, aren't as unapologetically indulgent as the recent films of Polish director Krzysztof Kieslowski. His DOUBLE LIFE OF VERONIQUE offers an oblique meditation on the properties of light alongside a metaphysical study of Veronique, a French woman who inadvertently photographs her perfect double (Weronika) while visiting Poland. BLUE casts art-house standby Juliette Binoche (THE UNBEARABLE LIGHTNESS OF BEING, DAMAGE) as a mopey French widow, and features a rich and overwhelming orchestral score and arresting imagery (a sugar cube touched to coffee fills the movie screen as the white crystals turn brown) that literally bring the film to a halt time and again. WHITE is a neatly comic film about marriage and capitalism that travels from France back to Poland and offers up at least one show-stopping visual metaphor--the screen blazes white in illustration of Julie Delpy's orgasm, reducing audiences to gasps and titters.
Kieslowski's harshest critics maintain that the films sap the sympathies of an irredeemably gullible audience. They accuse him of arranging for fashionable Frenchwomen to traipse through his very European landscapes, murmuring New Age platitudes, sleeping with sensitive New Age guys, and pouting for the camera. The director's newest film, RED, the culmination of his Three Colors trilogy which also includes BLUE and WHITE, they insist, is overblown claptrap, substituting notions of Fate and Destiny for credible filmmaking. The new issue of FILM COMMENT juxtaposes a rich essay on the trilogy by NEW YORK DAILY NEWS critic Dave Kehr with a tirade against it (by Phillip Lopate) which insists that the film's supporters have been aesthetically "bamboozled."
Kieslowski's audience is neither gullible nor easily amused. The concluding scenes of RED, which may represent the conclusion of the director's career, tie the three films of the trilogy together so perfectly and unexpectedly that their themes resonate in a viewer's head for days afterward. The films ostensibly examine the principles of liberty, equality, and fraternity, but they do so in the service of a much broader agenda. Kieslowski has integrated three very distinct pieces into a triptych that folds in on itself and contemplates its own nature. In the process, his characters determine one another's destinies, even as the director plays God.
Irene Jacob, the uncannily radiant star of VERONIQUE, is Valentine, a Swiss model who we see shooting an advertisement for (of all things) chewing gum. In the film's most memorable visual trope, her face is to be reproduced on a huge banner and draped against the side of a building in Geneva. Accidents and mishaps drive much of the action in Kieslowski's films, and the most important relationship here is catalyzed when Valentine, trying to tune in a distant radio station on her car stereo, hits a dog in the street. The dog, named Rita, is bleeding, but alive, and Valentine tries to take it back to its home. Valentine finds Rita's owner, the retired judge Joseph Kern (the perfectly crusty Jean-Louis Trintignant), who hardly seems to care whether the dog lives or dies. Kern leads a solitary life, but has a radio set up so that he can monitor the telephone conversations of his neighbors. Valentine initially finds his aural voyeurism repugnant, but a strange bond grows between the two characters as they relate their life stories to one another.
The film contains a great many telephone conversations, and indeed Kieslowski investigates the ways in which we humans communicate (or fail to communicate) with one another. It's telling that the relationship between Valentine and Kern (they could be lovers if not for their age difference) is the only one in which two people seem to be gaining knowledge and support from one another (Valentine has a boyfriend, Michel, who is in England and who she plans to visit, but throughout RED she only communicates with him by telephone). It would be unfair to give away further details, since much of the film is wrapped up in the intricate relationships that color the lives of both Valentine and Kern, and an odd sort of "double life" that Kern himself is living. The film, and the trilogy, culminate in an act of God which hinges on the intrusion of the director himself, who decides the final fate of his characters.
Although Kieslowski obviously finds something fascinating in the face of Irene Jacob (which is honestly the glue holding this film together), one is tempted to draw the conclusion that it's Trintignant's misanthropic old judge--who conspires to manipulate his own cast of characters--that the director feels the closest affinity with. It's a very good sign, then, that the embittered judge finds some measure of satisfaction and redemption at the end of _Red_ (which, incidentally, Kieslowski claims is his last film). Kern has a pointed conversation with Jacob at one point, asking her why she stopped to pick up the dog and take it to a doctor. Was it to help the dog, he wants to know, or was it to make herself feel better, less guilty? By the end of the film, the judge will be asking himself the same question about his own contrivance, one that is key for Kieslowski, who has said in interviews that he has come to believe people are inherently selfish.* Critics have complained that Kieslowski's films are reliant on coincidence and overblown ideas about Destiny, but it's a moot complaint when the director is so honest about his role as grand manipulator of his own world, weaving his presence thematically into the work. The culmination of his masterful Three Colors trilogy suggests there is Something Larger than Kieslowski's characters. Whether that is the Deity or simply the Director is left for us to decide.
* See the discussion of RED in Danusia Stok's excellent collection of interviews, KIESLOWSKI ON KIESLOWSKI, published in the US by Faber & Faber. ISBN: 0-571-16733-0.
-- Bryant Frazer 914/631/7155
.
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


Quiz App
This application has set of Python questions. This quiz is not a MCQ type , Here once you provide the answer and you can compare with our answers. Quiz application has a set of questions you can practice it all the questions at a time.If you are ready to go with this click the 'Start Quiz' button. If you want to refresh it daily you can subscribe to our Campaign question. We send an email daily to you if you click the link it will take you to the page and we display one question daily.


Autoroute: Tools > Frameworks > Metasploit > Autoroute
PortForward: Tools > Frameworks > Metasploit > Portfwd
Route: Tools > Frameworks > Metasploit > Route
SOCKS: See Tools > Frameworks > Metasploit > socks4a
Possible to set all of msfconsole to use proxychains SOCKS with external proxy?
Metasploit
You may also tunnel Metasploit® Framework exploits and modules through Beacon. Create a Beacon SOCKS proxy server [as described above] and paste the following into your Metasploit® Framework console:
setg Proxies socks4:team server IP:proxy port
setg ReverseAllowProxy true
These commands will instruct the Metasploit® Framework to apply your Proxies option to all modules executed from this point forward. Once you’re done pivoting through Beacon in this way, use unsetg Proxies to stop this behavior.


Post Title
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Hodzi-Kasukuwere case set for Friday
by Staff ReporterThe case in which Prosecutor–General (PG) Kumbirai Hodzi is contesting a High Court decision reversing the confiscation of former Cabinet minister Saviour Kasukuwere’s Nyanga mansion will be heard at the Supreme Court this Friday.
Hodzi lost his bid to take over the property after the High Court struck off his application which sought to bar the politician from controlling and disposing of the asset.
The High Court ordered immediate release of the former Zanu-PF politician’s title deeds after Justice Tawanda Chitapi cleared him of all the criminal offences he was facing.
Kasukuwere was temporarily given back his passport by the court to enable him to travel to South Africa for treatment but never came back.
This prompted the state to confiscate his holiday home he had pledged during bail.
The former Mount Darwin MP was facing several criminal charges and was yet to stand trial.
Aggrieved by the state’s move, the ex-minister took the case up with the High Court challenging confiscation of his property.
He won the case after Chitapi dismissed all charges he was facing.
The High Court gave another ruling releasing with immediate effect title deeds to Kasukuwere’s property, called Lot 4 of Subdivision D Manchester in the District of Umtali, Zimbabwe, registered under 8010/2003 which was held as surety when he was granted bail.
The ruling ordering release of his title deeds came at a time Hodzi had applied for leave to appeal against Chitapi’s in the Supreme Court under case number SC550/19.
Hodzi then filed an urgent application saying he feared Kasukuwere was going to dispose of his property, leaving him with no other recourse in case he wins the Supreme Court challenge.
But High Court judge, Benjamin Chikowero struck the case off the urgent roll before he ordered Hodzi to pay costs of suit to Kasukuwere.
Hodzi says the High Court erred in clearing a fugitive.
His office says Kasukuwere, being a fugitive from justice, had no right of audience in court.
Kasukuwere was supposed to return the passport on January 17 this year
Source – Daily News – newzimbabwe


Project Euler results
Problem 1 Result:
click to compute
Problem 2 Result:
click to compute
Problem 3 Result:
click to compute
Problem 4 Result:
click to compute
Problem 5 Result:
click to compute
Problem 6 Result:
click to compute
Problem 7 Result:
click to compute
Problem 8 Result:
click to compute
Problem 9 Result:
click to compute
Problem 10 Result:
click to compute
Problem 11 Result:
click to compute
Problem 12 Result:
click to compute
Problem 48 Result:
still working on this


November 23 2015
Bacon ipsum dolor amet ball tip alcatra salami cow ham hock venison. Flank tenderloin sausage strip steak meatball. Biltong venison leberkas buffalo bacon. Brisket spare ribs biltong flank, porchetta jowl ground round doner landjaeger buffalo short ribs meatloaf. Doner drumstick hamburger jowl alcatra.
Kevin bresaola cow jerky brisket t-bone porchetta ribeye pastrami. Cow turducken fatback beef, salami pork frankfurter sirloin drumstick leberkas prosciutto andouille pig. Capicola shoulder pork loin ham venison swine ground round andouille frankfurter drumstick ham hock pork boudin bresaola strip steak. Corned beef kevin kielbasa, short loin bacon pancetta cupim landjaeger pig frankfurter. Shankle filet mignon tail t-bone chicken tongue.
Pastrami ham hock swine biltong cow brisket tail hamburger jowl short loin sirloin sausage short ribs.  Jerky swine boudin filet mignon beef ribs bacon bresaola leberkas brisket cupim corned beef pig. Short loin jerky alcatra, pork chop flank hamburger burgdoggen strip steak meatloaf kielbasa doner brisket pork belly porchetta. Short loin pastrami beef ribs shoulder, cow pork prosciutto cupim.
December 11 2015
Burgdoggen tail fatback salami turkey. Frankfurter drumstick picanha meatloaf. Beef ribs tongue alcatra brisket pork belly. Pork belly pancetta fatback meatball rump burgdoggen kielbasa flank shank beef porchetta jerky ribeye corned beef short loin. Pancetta meatball beef ribs, bacon porchetta strip steak landjaeger pastrami kielbasa ball tip tail. Capicola beef ribs meatball, turducken tail strip steak porchetta andouille.
T-bone beef ribs short ribs, drumstick bresaola ribeye salami swine turducken cupim meatball boudin tail. Brisket capicola ball tip pork chop, sirloin chuck tail beef cow salami tongue flank. Pork loin ribeye cupim pancetta sausage beef ribs short ribs leberkas ground round pastrami t-bone ham shankle tongue. Landjaeger turducken biltong, frankfurter swine jowl turkey. Frankfurter kevin short loin swine sausage, tail pork loin shankle doner. Ground round tenderloin t-bone prosciutto, shoulder biltong tongue ham.
Does your lorem ipsum text long for something a little meatier? Give our generator a try… it’s tasty!


San Remo is about 20 miles east of Monaco, on the Cote d' Azur. The Old Town of San Remo is very interesting with building from the Roman period onwards.
All the authorities are at Marina Porto Sole.
The marina of Porto Sole is very well protected and has every modern facility. The old harbour, Porto Vecchio, is more crowded but has a wonderful Italian atmosphere.
Ten miles further east is the port of Maurizio. A new marina is being built here, but the existing port provides good shelter and services. It is also an ancient town with old streets and little winding alleys.


[ all classes ]
[ opennlp.tools.cmdline ]
Coverage Summary for Class: ModelLoader (opennlp.tools.cmdline)
| Class | Class, % | Method, % | Line, % |
|---|---|---|---|
| ModelLoader | 100% (1/ 1) | 100% (2/ 2) | 70% (14/ 20) |
1 /*
2 * Licensed to the Apache Software Foundation (ASF) under one or more
3 * contributor license agreements. See the NOTICE file distributed with
4 * this work for additional information regarding copyright ownership.
5 * The ASF licenses this file to You under the Apache License, Version 2.0
6 * (the "License"); you may not use this file except in compliance with
7 * the License. You may obtain a copy of the License at
8 *
9 * http://www.apache.org/licenses/LICENSE-2.0
10 *
11 * Unless required by applicable law or agreed to in writing, software
12 * distributed under the License is distributed on an "AS IS" BASIS,
13 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 * See the License for the specific language governing permissions and
15 * limitations under the License.
16 */
17
18 package opennlp.tools.cmdline;
19
20 import java.io.BufferedInputStream;
21 import java.io.File;
22 import java.io.IOException;
23 import java.io.InputStream;
24 import java.util.Objects;
25
26 import opennlp.tools.util.InvalidFormatException;
27
28 /**
29 * Loads a model and does all the error handling for the command line tools.
30 *

31 * Note: Do not use this class, internal use only!
32 *
33 * @param <T>
34 */
35 public abstract class ModelLoader<T> {
36
37 private final String modelName;
38
39 protected ModelLoader(String modelName) {
40 this.modelName = Objects.requireNonNull(modelName, "modelName must not be null!");
41 }
42
43 protected abstract T loadModel(InputStream modelIn) throws IOException;
44
45 public T load(File modelFile) {
46
47 long beginModelLoadingTime = System.currentTimeMillis();
48
49 CmdLineUtil.checkInputFile(modelName + " model", modelFile);
50
51 System.err.print("Loading " + modelName + " model ... ");
52
53 T model;
54 try (InputStream modelIn = new BufferedInputStream(
55 CmdLineUtil.openInFile(modelFile), CmdLineUtil.IO_BUFFER_SIZE)) {
56 model = loadModel(modelIn);
57 }
58 catch (InvalidFormatException e) {
59 System.err.println("failed");
60 throw new TerminateToolException(-1, "Model has invalid format", e);
61 }
62 catch (IOException e) {
63 System.err.println("failed");
64 throw new TerminateToolException(-1, "IO error while loading model file '" + modelFile + "'", e);
65 }
66
67 long modelLoadingDuration = System.currentTimeMillis() - beginModelLoadingTime;
68
69 System.err.printf("done (%.3fs)\n", modelLoadingDuration / 1000d);
70
71 return model;
72 }
73 }


tock
/
ai.tock.bot.connector.alexa
/
AlexaMessage
/
card
card
val
card
:
Card
?
(source)
Is there a card to send?


| 1 | ||
| 2 | package paavohuh.sourcream.utils; | |
| 3 | ||
| 4 | import java.io.InputStream; | |
| 5 | import java.util.Optional; | |
| 6 | import org.apache.commons.io.IOUtils; | |
| 7 | ||
| 8 | /** | |
| 9 | * Contains utility methods for working with JAR resources. | |
| 10 | */ | |
| 11 | public class ResourceUtils { | |
| 12 | /** | |
| 13 | * Gets a JAR resource as a string. | |
| 14 | * @param name The path to the resource. | |
| 15 | * @return Optional containing the resource if the file exists and is valid | |
| 16 | * UTF-8. | |
| 17 | */ | |
| 18 | public static Optional<String> getAsString(String name) { | |
| 19 | InputStream stream = ResourceUtils.class.getResourceAsStream(name); | |
| 20 |
2
1. getAsString : mutated return of Object value for paavohuh/sourcream/utils/ResourceUtils::getAsString to ( if (x != null) null else throw new RuntimeException ) → NO_COVERAGE 2. lambda$getAsString$5 : mutated return of Object value for paavohuh/sourcream/utils/ResourceUtils::lambda$getAsString$5 to ( if (x != null) null else throw new RuntimeException ) → NO_COVERAGE |
return OptionalUtils.ofThrowing(() -> IOUtils.toString(stream, "UTF-8")); |
| 21 | } | |
| 22 | | |
| 23 | /** | |
| 24 | * Gets a JAR resource as a byte array. | |
| 25 | * @param name The path to the resource. | |
| 26 | * @return Optional containing the resource if the file exists. | |
| 27 | */ | |
| 28 | public static Optional<byte[]> getAsBytes(String name) { | |
| 29 | InputStream stream = ResourceUtils.class.getResourceAsStream(name); | |
| 30 |
2
1. getAsBytes : mutated return of Object value for paavohuh/sourcream/utils/ResourceUtils::getAsBytes to ( if (x != null) null else throw new RuntimeException ) → KILLED 2. lambda$getAsBytes$6 : mutated return of Object value for paavohuh/sourcream/utils/ResourceUtils::lambda$getAsBytes$6 to ( if (x != null) null else throw new RuntimeException ) → KILLED |
return OptionalUtils.ofThrowing(() -> IOUtils.toByteArray(stream)); |
| 31 | } | |
| 32 | } | |
Mutations | ||
| 20 |
1.1 2.2 |
|
| 30 |
1.1 2.2 |


Protect your keyboard from dust and bump!
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class: center, middle, inverse, title-slide # Fitting ### Applied Machine Learning with R
Basel R Bootcamp
### January 2019 --- layout: true
        www.therbootcamp.com      Applied Machine Learning with R | January 2019
 --- # Where we are at .pull-left4[ - Have a business <high>question</high>: How can I predict loan default? - Have <high>data</high> relevant to that question: Records from 300 historical customers. - Data is cleaned and in a <high>tidy</high>, rectangular format: Database, .csv.
 ### What's next? <high>Select</high> and <high>train</high> model(s) depending on the <high>type of task</high> ] .pull-right55[ <br2>

Source: Medium.com

 ] --- # What type of task do you have? .pull-left45[ There are <high>many types of of ML tasks</high>. In this course, we will focus on 2 of the most popular
 |Type|Description | Example | |:----|:---- |:----| |<high>Regression</high> (supervised)|Predicting a number|Stock prices| |<high>Classification</high> (supervised)|Predicting a category, like whether|Whether someone will purchase a product or not| ] .pull-right5[  ] --- .pull-left45[ # What ML models are there?
 There are *thousands* of machine learning models In this course, you will learn 3 of the most popular: |Model|Description| |:----|:---- | |<high>Regression</high>|A weighted linear combination of features and weights| |<high>Decision Tree</high>|A series of hierarchical 'yes/no' decisions| |<high>Random Forests</high>|Combination of many decision trees| ] .pull-right5[
<br2>  ] --- class: center, middle ## Once you have a model you need to "Fit" (aka, "Train") it to data. <high>What does that mean?</high>

 <img src="https://afit-r.github.io/public/images/analytics/regression/sq.errors-1.png" alt="James et al., Introduction to SL" width="50%" />
James et al., Introduction to SL

 --- # What does "Fitting" mean? .pull-left5[ For any model class, there is no "One" single model. > How many possible Regression models are there? <br2> Any machine learning model can be 'fit' (aka 'trained') on a specific dataset of interest. This means <high>finding the "best" version of a model</high> for a specific dataset. > "Let me represent the data in the best way I can given how I work"
 >~ Model during fitting <br2> The "best" model is usually defined as a combination of <high>accuracy</high> (higher better!) and <high>complexity</high> (simpler is better!) ] .pull-right45[
<high>How do I fit a model to these data?</high>
  ] --- # Defining Accuracy (or Error) .pull-left45[ To train (fit) a model to a dataset, we need to <high>mathematically define Accuracy</high> Alternatively, we can define a model's <high>Error</high> There is <high>no 'correct'</high> definition of error, it depends on <high>what's important to you</high> as the decision maker! Once accuracy (or error) is defined, a model can be trained to maximize (or minimize) it! The model that minimizes error (or maximizes accuracy) is the final <high>Training model</high> ] .pull-right45[
<high>How do I fit a model to these data?</high>
  ] --- # Which of these models is better? Why?  --- # Which of these models is better? Why?  --- # Regression Error .pull-left45[ #### MAE: Mean Absolute Error `$$\large MAE = \frac{1}{n}\sum_{i=1}^{n} \lvert Prediction_{i} - Truth_{i} \rvert$$` > On average, how far are predictions away from true values? #### MSE: Mean Squared Error `$$\large MSE = \frac{1}{n}\sum_{i=1}^{n}(Prediction_{i} - Truth_{i})^{2}$$` > On average, how far are predictions away from true values (squared!)? ] .pull-right5[  ] --- .pull-left45[ # Classification Accuracy Classification accuracy measures all come from the <high> "confusion matrix"</high> The confusion matrix is a cross tabulation table showing predictions versus true classes. #### Confusion Matrix | | Y is Positive | Y is Negative | |--------------|:-------------:|:------:| | Predict
"Positive"| TP
 True Positive| FP
 False Positive | | Predict
"Negative"| FN
 False Negative | TN
 True Negative| Note: Green cells are correct decisions while Red cells are incorrect decisions ] .pull-right5[
 #### Data ||X1|X2|X3|Prediction|Truth|Outcome| |:---|:----|:----|:----|:----|:----|:----| |1|.|.|.|"Default"|Default|TP| |2|.|.|.|"Default"|Default|TP| |3|.|.|.|"Repay"|Repay|TN| |4|.|.|.|"Default"|Repay|FP| |5|.|.|.|"Repay"|Default|FN| |6|.|.|.|"Default"|Default|TP| |7|.|.|.|"Repay"|Repay|TN| #### Confusion Matrix | | True Default | True Repay | |------------------|:-------------:|:------:| | "Default"| 3| 1  | | "Repay"| 1 |  2| ] --- .pull-left45[ # Classification Accuracy Classification accuracy measures all come from the <high> "confusion matrix"</high> The confusion matrix is a cross tabulation table showing predictions versus true classes. #### Confusion Matrix | | Y is Positive | Y is Negative | |-----------------------|:-------------:|:------:| | Predict
"Positive"| TP
 True Positive| FP
 False Positive | | Predict
"Negative"| FN
 False Negative | TN
 True Negative| Note: Green cells are correct decisions while Red cells are incorrect decisions ] .pull-right5[
 #### Overall Accuracy > What percent of my predictions are correct? `$$\large Overall \; Accuracy = \frac{TP + TN}{ TP + TN + FN + FP}$$` #### Sensitivity > Of the truly Positive cases, what percent of predictions are correct? `$$\large Sensitivity = \frac{TP}{ TP +FN }$$` #### Specificity > Of the truly Negative cases, what percent of predictions are correct? `$$\large Specificity = \frac{TN}{ TN + FP }$$` ] --- .pull-left45[ # Classification Accuracy ### Example: Loan default Imagine we use a model (e.g. a decision tree) to predict whether or not each of 7 customers will default on their loan. After the loan period is over, we obtain the final confusion matrix comparing our predictions to the truth: ### Confusion Matrix | | True Default | True Repay | |---------------------|:-------------:|:------:| | Predict
"Default"| TP
3| FP
1  | | Predict
"Repay"| FN
1 | TN
 2| ] .pull-right5[
 #### Overall Accuracy Across all customers, our model has an accuracy of 71% `$$\large Overall \; Accuracy = \frac{3 + 2}{3 + 2 + 1 + 1} = 0.71$$` #### Sensitivity Our model is 75% accurate in catching true defaults `$$\large Sensitivity = \frac{3}{3 + 4} = .75$$` #### Specificity Our model is 67% accurate in catching true repayments `$$\large Specificity = \frac{2}{ 2 + 1 }= 0.67$$` ] --- .pull-left45[ # Ready to fit! Now we're ready to fit models to data! In this course will cover three commonly used models, <high>Regression</high>, <high>Decision Trees</high>, and <high>Random Forest</high>. These models can be used in both regression and classification tasks. As you'll see, they differ in complexity in important regards. |Model|Complexity| |:----|:---- | |Regression|Medium| |Decision Tree|Low (usually)| |Random Forests|High| ] .pull-right5[
  ] --- # Model Training (aka fitting)  --- class: center, middle <high>
Regression
</high>
Decision Trees

Random Forests
 --- # Regression .pull-left4[ In [regression](https://en.wikipedia.org/wiki/Regression_analysis), the criterion Y is modeled as the <high>sum</high> of <high>predictors times weights</high> `\(\beta_{1}\)`, `\(\beta_{2}\)`</high>. `$$\hat{Y} = \beta_{0} + X1 \times \beta_{X1} + X2 \times \beta_{X2} + ...$$`

 <img src="https://afit-r.github.io/public/images/analytics/regression/sq.errors-1.png" alt="James et al., Introduction to SL" width="100%" />
James et al., Introduction to SL

 ] .pull-right55[ #### Interpretation `$$\LARGE \hat{Y} = \beta_{0} + X1 \times \beta_{X1} + X2 \times \beta_{X2} + ...$$` Each beta weight `\(\beta_{i}\)` can be interpreted as: <q>As the value of `\(X_{i}\)` increases by 1, how does the criterion `\(Y\)` change?</q> The more extreme `\(\beta_{i}\)` is (either positive or negative), the more `\(X_{i}\)` is used to predict the criterion `\(Y\)` (Note: take into account the scale of `\(X_{i}\)`!). If a value of `\(\beta_{i}\)` is exactly 0, that means `\(X_{i}\)` does not help us predict the criterion `\(Y\)` ] --- # Regression .pull-left4[ In [regression](https://en.wikipedia.org/wiki/Regression_analysis), the criterion Y is modeled as the <high>sum</high> of <high>predictors times weights</high> `\(\beta_{1}\)`, `\(\beta_{2}\)`</high>. `$$\hat{Y} = \beta_{0} + X1 \times \beta_{X1} + X2 \times \beta_{X2} + ...$$`

 <img src="https://afit-r.github.io/public/images/analytics/regression/sq.errors-1.png" alt="James et al., Introduction to SL" width="100%" />
James et al., Introduction to SL

 ] .pull-right55[ ### Sales Example  Regression Model `$$\large Sales = \beta_{0} + CompPrice \times \beta_{CompPrice} + Income \times \beta_{Income} + ...$$` Estimates `$$\large Sales = 10 + CompPrice \times 5.4 + Income \times 1.3 + ...$$` ] --- class: center, middle

 # Let's fit regression models with caret!  --- .pull-left4[ # `caret` Main caret fitting functions | Function| Purpose| |--------|----------| | [trainControl()](http://topepo.github.io/caret/model-training-and-tuning.html#basic-parameter-tuning) | Determine how training (in general) will be done| | [train()](http://topepo.github.io/caret/model-training-and-tuning.html#model-training-and-parameter-tuning) | Specify a model and find *best* parameters| | predict() | Predict values (either fitted values or predictions for new data)| | [postResample()](http://topepo.github.io/caret/measuring-performance.html) | Evaluate model performance (fitting or prediction)| ] .pull-right5[
 ```r # Step 1: Load data # read_csv() data_train <- read_csv(...) # Step 2: Define control parameters # trainControl() ctrl <- trainControl(...) # Step 3: Train and explore model # train() mod <- train(...) summary(mod) mod$finalModel # see final model # Step 4: Assess fit # predict(), postResample() fit <- predict() postResample(fit, truth) # Step 5: Visualise results ggplot(...) ```   ] --- # `trainControl()` .pull-left55[ Use `trainControl()` to define how `caret` should, generally, <high>select the best parameters</high> for an ML model. Here you can tell `caret` to do things like repeated <high>cross validation</high> (which we will learn about later). |Argument|Description| |:-----|:----| |`method`|How should fitting be done?| For now, we'll set `method = "none"` to keep things simple to <high>fit the model without advanced parameter tuning</high>. ```r # Fit the model without any # advanced parameter tuning methods ctrl <- trainControl(method = "none") ``` ] .pull-right4[ ```r ?trainControl ```  ] --- # `train()` .pull-left4[ `train()` is the workhorse fitting function of `caret`. With just this one function, you can <high>fit any of 200+ models</high> just by changing the <high>method</high> argument! |Argument|Description| |:-----|:----| |`form`|Formula specifying criterion| |`data`|Training data| |`method`| Model| |`trControl`| Control parameters| ] .pull-right55[ #### Train a Regression model Regression: `method = "glm"` ```r # Fit a regression model predicting Price mod <- train(form = income ~ ., # Formula data = baselers, # Training data method = "glm", # Regression trControl = ctrl) # Control Parameters ``` ] --- # `train()` .pull-left4[ `train()` is the workhorse fitting function of `caret`. With just this one function, you can <high>fit any of 200+ models</high> just by changing the <high>method</high> argument! |Argument|Description| |:-----|:----| |`form`|Formula specifying criterion| |`data`|Training data| |`method`| Model| |`trControl`| Control parameters| ] .pull-right55[ #### Train a Random Forest model Random Forest: `method = "rf"` ```r # Fit a Random Forest model predicting Price mod <- train(form = income ~ ., # Formula data = baselers, # Training data method = "rf", # Random Forests trControl = ctrl) # Control Parameters ``` ] --- .pull-left4[ # `train()` `train()` is the workhorse fitting function of `caret`. With just this one function, you can <high>fit any of 200+ models</high> just by changing the <high>method</high> argument! Find all 280+ models [here](http://topepo.github.io/caret/available-models.html). ] .pull-right55[
  ] --- .pull-left4[ # `train()` Make sure your criterion is the correct class for your type of modelling task - Numeric criterion = Regression Task - Factor criterion = Classification Task ```r # My training data Loans ``` ``` # A tibble: 5 x 5 Default Age Gender Cards Education <dbl> <dbl> <chr> <dbl> <dbl> 1 0 45 M 3 11 2 1 36 F 2 14 3 0 76 F 5 12 4 1 25 M 2 17 5 1 36 F 3 12 ``` See that the column Default is 0's and 1's, but is coded as numeric. ] .pull-right55[

 This code will think that Default is a continuous number, not a category (probably not what you want) ```r # Will be a regression task if Default is numeric mod <- train(form = default ~ ., data = Loans, method = "glm", trControl = ctrl) ``` Warning messages:...Are you sure you wanted to do regression? Use `factor()` to <high>convert your criterion</high> to a factor, now you are doing classification! ```r # Will be a classification task mod <- train(form = factor(Default) ~ ., data = Loans, method = "glm") ``` ] --- .pull-left35[ # .$finalModel The `train()` function returns a list with a key object called `finalModel` - this is your final machine learning model! You can access the model with `mod$finalModel`, and explore the object with generic functions: |Function|Description |:------|:----| |`summary()`| Overview of the most important information| |`names()`|See all named elements you can access with $| ] .pull-right6[
 ```r # Create a regression object mod <- train(form = income ~ age + height + fitness, data = baselers) # Training data # Look at final model mod$finalModel # [...] ``` ```r # Look at all named outputs names(mod$finalModel) ``` ``` [1] "coefficients" "residuals" "fitted.values" "effects" "R" [6] "rank" "qr" "family" "linear.predictors" "deviance" [11] "aic" "null.deviance" "iter" "weights" "prior.weights" [16] "df.residual" "df.null" "y" "converged" "boundary" [21] "model" "formula" "terms" "data" "offset" [26] "control" "method" "contrasts" "xlevels" "xNames" [31] "problemType" "tuneValue" "obsLevels" "param" ``` ```r # Access specific outputs mod$finalModel$coefficients ``` ``` (Intercept) age height fitness 136.606 151.751 3.381 11.012 ``` ] --- # `predict()` .pull-left5[ The `predict()` function is allows you to return predictions from a model. Put your model object as the first argument. If you don't specify a new dataset with `newdata`, the function returns <high>fitted values from training</high> ```r # Get fitted values glm_fits <- predict(object = mod) ``` The result is a vector ```r # Result is a vector of fits glm_fits[1:5] ``` ``` 1 2 3 4 5 5507 6971 6969 8643 5324 ``` ] .pull-right45[ #### Plot of fits versus Truth If the model was perfect, all points would be on diagonal  ] --- # `postResample()` .pull-left5[ To calculate aggregate model performance, use `postResample()` |Argument|Description| |:----|:----| |pred|Model predictions (or fits)| |obs|The observed (true) values|
 ```r # Assess performance with postResample() postResample(pred = glm_fits, # Predictions obs = baselers$income) # Truth ``` ``` RMSE Rsquared MAE 1172.905 0.821 936.994 ``` ] .pull-right45[ #### Plot of fits versus Truth Red lines indicate absolute error(s)  ] --- class: middle, center
 Questions?

Practical


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AWS::EC2::VPNGatewayRoutePropagation
Enables a virtual private gateway (VGW) to propagate routes to the routing tables of a VPC.
Note
If you reference a VPN gateway that is in the same template as your VPN gateway route
propagation, you must explicitly declare a dependency on the VPN gateway attachment.
The
AWS::EC2::VPNGatewayRoutePropagation
resource cannot use the VPN gateway
until it has successfully attached to the VPC. Add a DependsOn attribute in the
AWS::EC2::VPNGatewayRoutePropagation
resource to explicitly declare a
dependency on the VPN gateway attachment.
Topics
Syntax
To declare this entity in your AWS CloudFormation template, use the following syntax:
JSON
{ "Type" : "AWS::EC2::VPNGatewayRoutePropagation", "Properties" : { "RouteTableIds" : [
String, ...
], "VpnGatewayId" :String
} }
YAML
Type: AWS::EC2::VPNGatewayRoutePropagation Properties: RouteTableIds: -
String
VpnGatewayId:String
Properties
RouteTableIds
-
A list of routing table IDs that are associated with a VPC. The routing tables must be associated with the same VPC that the virtual private gateway is attached to.
Required: Yes
Type: List of route table IDs
Update requires: No interruption
VpnGatewayId
-
The ID of the virtual private gateway that is attached to a VPC. The virtual private gateway must be attached to the same VPC that the routing tables are associated with.
Required: Yes
Type: String
Update requires: No interruption
Return Value
When the logical ID of this resource is provided to the Ref
intrinsic
function, Ref
returns the resource name. For example:
{ "Ref": "
myVPNGatewayRouteProp
" }
For the VPN gateway with the logical ID
,
myVPNGatewayRouteProp
Ref
will return the gateway's resource name.
For more information about using the Ref
function, see Ref.
Example
JSON
"myVPNGatewayRouteProp" : { "Type" : "AWS::EC2::VPNGatewayRoutePropagation", "Properties" : { "RouteTableIds" : [{"Ref" : "PrivateRouteTable"}], "VpnGatewayId" : {"Ref" : "VPNGateway"} } }
YAML
myVPNGatewayRouteProp: Type: AWS::EC2::VPNGatewayRoutePropagation Properties: RouteTableIds: - !Ref PrivateRouteTable VpnGatewayId: !Ref VPNGateway
See Also
-
EnableVgwRoutePropagation in the Amazon EC2 API Reference.


This file contains samples and information for CSLU: Kids' Speech Version 1.1 , Linguistic Data Consortium (LDC) catalog number LDC2007S18 and isbn 1-58563-395-X.
CSLU Kids' Speech Version 1.1 is a collection of spontaneous and prompted speech from 1100 children between Kindergarten and Grade 10 in the Forest Grove School District in Oregon. All children -- approximately 100 children at each grade level -- read approximately 60 items from a total list of 319 phonetically-balanced but simple words, sentences or digit strings. Each utterance of spontaneous speech begins with a recitation of the alphabet and contains a monologue of about one minute in duration. This release consists of 1017 files containing approximately 8-10 minutes of speech per speaker. Corresponding word-level transcriptions are also included.
This corpus was developed to facilitate research about the characteristics of children's speech at different ages and to train and evaluate recognizers for use in language training and other interactive tasks involving children, including to train recognizers used in language development with deaf children.
Data collection was performed using the CSLU Speech Toolkit and two computers running Windows NT 4.0. Each computer was manned by a CSLU staff member who monitored progress and helped the child with any difficulties. The average time at the computer was 20 minutes, yielding approximately 8-10 minutes of speech digitized at 16 bits and 16kHz using Soundblaster 16 PnP audio cards with head-mounted microphones.
The prompted speech, consisting of 200 isolated words and 10 numeriuc strings, was presented as text appearing below an animated character that produced accurate visible speech synchronized with recorded prompts. A text prompt was also displayed. The child then reproduced the prompted word. Once the prompted speech collection was completed, the experimenter then asked the subject a series of questions designed to elicit spontaneous speech (i.e "Tell me about your favorite movie"). Information about the subject's age, gender, languages spoken and physical conditions affecting speech were also collected.
Presented here are two samples, one of spontaneous speech and one of scripted speech, with their transcriptions.
Portions © 2001-2002 Center for Spoken Language Understanding, Oregon Health & Science University, © 2007 Trustees of the University of Pennsylvania


BARRIER REEF ORCHESTRA MEMBERSHIP FORM
Membership Application North Queensland Ensembles Inc PO Box 576, Townsville 4810
tel. 07 4722 2789 email<EMAIL_ADDRESS>A.B.N. 73 365 249 138 North Queensland Ensembles Inc. is the administration body of the Barrier Reef Orchestra, by becoming a member you are supporting Townsville’s Community Orchestra which was formed in 2000.


--- layout: post status: publish published: true title: Filenames Starting with a Dot author: display_name: Tomas Restrepo login: tomasr email<EMAIL_ADDRESS>url: http://winterdom.com/ author_login: tomasr author_email<EMAIL_ADDRESS>author_url: http://winterdom.com/ wordpress_id: 186 wordpress_url: http://winterdom.com/2008/02/filenamesstartingwithadot date: '2008-02-25 13:25:03 +0000' date_gmt: '2008-02-25 13:25:03 +0000' categories: - Vista tags: [] comments: - id: 134 author: Damien Guard author_email<EMAIL_ADDRESS>author_url: http://damieng.com date: '2008-02-25 15:08:30 +0000' date_gmt: '2008-02-25 15:08:30 +0000' content: | Visual Studio 2003 used to be very troublesome with .folders and files which was a real pain as Subversion created .svn folders for the metadata by default. Most Windows based Subversion tools had the ability to switch this over to _svn for exactly that reason. [)amien - id: 135 author: Tomas Restrepo author_email<EMAIL_ADDRESS>author_url: http://www.winterdom.com/weblog/ date: '2008-02-25 15:28:51 +0000' date_gmt: '2008-02-25 15:28:51 +0000' content: | Wasn't ASP.NET the one causing trouble? At least I remember reading something about it, though never caused me any trouble (or maybe it is IIS, trying to figure out the right mime type for it or something). Either way, anyway you look at it, it is those tools/apps that are broken; not the file system semantics. - id: 136 author: Flatliner author_email<EMAIL_ADDRESS>author_url: '' date: '2008-03-10 15:26:05 +0000' date_gmt: '2008-03-10 15:26:05 +0000' content: | I believe Windows Explorer disallows filenames starting with a . because it's default setting of hiding File Extensions. It deems .txt (for example) as just an extension so nothing would show after you named a file like this. - id: 137 author: Tomas Restrepo author_email<EMAIL_ADDRESS>author_url: http://www.winterdom.com/weblog/ date: '2008-03-10 15:42:13 +0000' date_gmt: '2008-03-10 15:42:13 +0000' content: | @Flatliner: That might very well be true; but, heck, I *always* disable that option as soon as I install windows; it's the most stupid setting anyone ever came up with. - id: 138 author: Flatliner author_email<EMAIL_ADDRESS>author_url: '' date: '2008-03-11 15:13:08 +0000' date_gmt: '2008-03-11 15:13:08 +0000' content: | Oh totally agree on that point, I also think it's a security risk to hide file extensions from users. (remember the old .vbs attachment viruses?). I was really hoping that setting would be off in Vista by default as part of MS's whole "secure by default" strategy. Sadly not. - id: 139 author: Tomas Restrepo author_email<EMAIL_ADDRESS>author_url: http://www.winterdom.com/weblog/ date: '2008-03-11 15:17:56 +0000' date_gmt: '2008-03-11 15:17:56 +0000' content: | I'm far more surprised that setting is still enabled by default on Windows Server 2008. - id: 140 author: George V. Reilly author_email<EMAIL_ADDRESS>author_url: http://www.georgevreilly.com/blog/ date: '2008-03-25 23:29:15 +0000' date_gmt: '2008-03-25 23:29:15 +0000' content: | I wrote a lot of the Vim Win32 console mode code back in the mid-90s. If I remember correctly, NT 3.x supported FAT-16. I'm pretty sure Win95 (cursed be its name) did. And Bram had a policy at the time of trying to unify the Win32 and the DOS codebases as much as possible. A filename like .vimrc wouldn't have worked on FAT-16. (Or something like that. I had to page these memories back in from tape, where they were stored with a lossy compressor.) - id: 141 author: Tomas Restrepo author_email<EMAIL_ADDRESS>author_url: http://www.winterdom.com/weblog/ date: '2008-03-26 15:25:24 +0000' date_gmt: '2008-03-26 15:25:24 +0000' content: | George: I wasn't singling VIM out, just using it as an example. And I do understand it was because of old FAT compatibility and stuff like that (which does make you wonder how long we'll carry that noose around our necks). And, btw, thanks for all the work on VIM :) - id: 1821 author: Max author_email<EMAIL_ADDRESS>author_url: '' date: '2011-10-26 18:37:34 +0000' date_gmt: '2011-10-26 18:37:34 +0000' content: "I actually found a way to do this !\r\n\r\nIn my example, i'll try to make a file named .nomedia (for Android app purposes :p )\r\n\r\nI create a file, i name it \".nomedia.txt\". I rename it and i remove the \"txt\" so the name is about to be called \".nomedia.\", and i press enter. Windows will automatically remove the extra dot and you will have your .nomedia file ^^" - id: 10445 author: Charles Lee author_email<EMAIL_ADDRESS>author_url: '' date: '2013-03-11 20:32:45 +0000' date_gmt: '2013-03-11 20:32:45 +0000' content: "Thanks Max. I found out you can also do it at the command line in Win7. \ Needed .RHistory file for the R statistical package. First I named it with the .txt extension, then at the command line: \r\n ren .Rhistory.txt .RHistory," - id: 70500 author: Michael author_email<EMAIL_ADDRESS>author_url: http://www.alceproject.net/ date: '2014-02-04 15:35:53 +0000' date_gmt: '2014-02-04 15:35:53 +0000' content: "Just wanted to point out, you don't even have to create and then rename the file...\r\n\r\nAt least on Windows 7, it works quite nicely as long as you add in the extra period. ;)\r\n\r\nMakes me wonder if this is a hidden \"feature\", something the Windows devs thought could give us the best of both worlds—prevent unknowing \"novice\" users from creating file names that would disappear under default settings, while still allowing \"power\" users to create files beginning with dot, as long as they *acknowledged* they were doing this with the ending dot.\r\n\r\nThough I still feel that, if this were really intended to protect novices from Windows' own \"hide file extensions\" feature, it should at least check to see if the option were enabled before yelling at the user. I too agree that the \"hide file extensions\" feature is repulsive; especially for novices, it lulls people into a false sense of security and doesn't help them determine if that innocent-looking JPEG image is really an executable file with the Windows default JPEG icon as its executable icon." ---
In Unix, it's common to name files and folders with names starting with a '.' to make them "hidden". This convention is not really needed in Windows, since there's a file system attribute for that, but still interesting.
See, a few tools ported from Unix that relied on this convention use a different one in Windows, replacing the '.' with a '_'. So, for example, your Vim settings file will be "_vimrc" instead of ".vimrc" and so on. Seems pretty obvious they did this because at some point in time file/folder names could not start with a dot.
Windows 9x probably had this limitation, and probably still lives on in FAT-based file systems (but I don't have one anywhere to test this assertion, nor do I particularly care about it). However, modern Windows and NTFS have no such limitation. Almost.
See, NTFS certainly let's you create a folder named ".vimfiles" or a file named ".txt" for that matter. You can do so programatically using the standard file handling APIs. You can do it from the command line using mkdir/echo.
But you can't do it using Windows Explorer. For example, Vista and WinServer2k8 will complain that "You must type a filename" if you attempt to name a folder/file with a dot as the first character in the name.
Why Explorer explicitly disallows it, I'm not sure, but seems pretty funky. There are probably thousands of applications or utilities out there that might get confused with a file or folder named like that, though.


Utility maintaining the INF.SYS file. The INF.SYS file holds the users' database. In case of problem in INF.SYS file, goto the SYSTEM directory and run MAINTINF followed by a number of months. The records of users not connected within this number of months will be deleted and these users will be asked as for a new connection. The number 0 will not delete any record and only check the INF.SYS file. This page was last updated


In Ruby an array is ordered, integer-indexed collections of any object, arrays are represented as objects. It can hold multiple items called elements of the array. Each element is an array is associated with and referred to by an index. Array indexing starts at 0, for example put name [0] will print out the first name and put name [1] will print out the second name. You can make arrays act like sets, stacks or queues and they do not need to consist of the same data types.
Hashes in ruby are similar to arrays in that they are indexed collection of object references. However while you index arrays with integers, you can index a hash with objects of any type of strings, regular expressions, etc. Hashes enumerate their values in the order that the corresponding keys were inserted. You can store a value in a hash, two objects using a key and the value. Hashes have a default value that is returned when accessing keys that do not exist in the hash, when an attempt is made to access keys that do not exist in the hash it will be known as nil. The main difference between a hash and a array is that compared with arrays, hashes have one significant advantage: they can use any object as an index.


Panel bats for decriminalizing more provisions of Companies Act
New Delhi: The Company Law Committee on Monday submitted its recommendations to Finance Minister Nirmala Sitharaman on providing easier functioning for corporates in the country by further decriminalizing the provisions of the Companies Act 2013.
The committee was set up in September by the Corporate Affairs Ministry as part of a larger programme to provide ease of functioning for the corporate sector where sentiments have been dampened on account of multifarious provisions in existing corporate laws that criminalize several business activities even if these are petty and harmless in nature.
The committee took note of the progress made consequent to the Companies (Amendment) Act, 2019, which had resulted in the decriminalisation of 16 minor procedural and technical lapses under the Companies Act, 2013, into “civil wrongs”, the Company Law Committee said in its report.
It has adopted a principle-based approach to further remove criminality in case of default which can be determined objectively and which, otherwise, lack the element of fraud or do not involve larger public interest, the committee said.
The Committee has proposed amendments in 46 penal provisions, so as to either remove criminality, or to restrict the punishment to only fine, or to allow rectification of defaults through alternative methods, which would lead to further de-clogging of the criminal justice system in the country.
It has proposed re-categorizing 23 offences out of the 66 remaining compoundable offences under the Act, to be dealt with within the in-house adjudication framework wherein these defaults would be subject to a penalty levied by the adjudicating officer. (IANS)
Also Read: Jet Airways First Indian Company to Face Cross-Border Insolvency Protocol
Also Watch: Govt all set to push for the contentious Citizenship Bill in the winter session of Parliament


-
Total Subjects :
7
-
Total Students :
150
-
Total Classrooms :
7
-
Total Teachers :
7
-
Students : 50
-
Teacher : Safi
-
Performance Graph (Optional)
-
Classes :
9th, 10th
-
Subjects :
Maths, Physics
-
Recorder Lectures :
10
-
Live Lectures : 16
-
Total Students : 50
-
Delivered Lectures :
15
- Performance Graph
-
Teacher : Tuba
-
Attendance : 15/30
-
Assignments : 2
-
Quizzes : 2
- Performance Graph


DTN 2019
@mafintosh
Dat protocol lead
+ ---------------------------+
Dat ~= | Dat cli, dat-js, etc |
+ ----------+----------------+
| Other | Hyperdrive |
+ ----------+----------------+
| Discovery | Hypercore |
+ ----------+----------------+
+ ---------------------------+
Dat ~= | Dat cli, dat-js, etc |
+ ----------+----------------+
| Other | Hyperdrive |
+ ----------+----------------+
| Discovery | Hypercore |
+ ----------+----------------+
+ ---------------------------+
Dat ~= | Dat cli, dat-js, etc |
+ ----------+----------------+
| Other | Hyperdrive |
+ ----------+----------------+
| Discovery | Hypercore |
+ ----------+----------------+
5 Person Core Team
andrewosh, mafintosh, pfrazee, emilbayes, davidmarkclements
+ ---------------------------+
Dat ~= | Dat cli, dat-js, etc |
+ ----------+----------------+
| Other | Hyperdrive |
+ ----------+----------------+
| Discovery | Hypercore |
+ ----------+----------------+
Our core distributed append-only log
+-----------+
| hypercore |
+-----------+
+-----------+
| hypercore |
+-----------+
| entry #0 |
+-----------+
| hypercore |
+-----------+
| entry #0 |
+-----------+
| entry #1 |
+-----------+
| hypercore |
+-----------+
| entry #0 |
+-----------+
| entry #1 |
+-----------+
| entry #2 |
\0/
| <----> \0/
| <---> \0/
|
HSM support
Noise
Updated wire protocol
Status:
Stable and battle tested.
+ ---------------------------+
Dat ~= | Dat cli, dat-js, etc |
+ ----------+----------------+
| Other | Hyperdrive |
+ ----------+----------------+
| Discovery | Hypercore |
+ ----------+----------------+
Hyperdrive is our file system abstraction
Implements a distributed POSIX* FS, focused on real time updates
Currently uses a 2 Hypercore design
1 metadata Hypercore
1 content Hypercore
Metadata hypercore:
0: [header with a link to the content feed]
1: name: foo.txt, contentPtr: 0, blocks: 10, [lookup-tree-index]
2: name: bar.txt, contentPtr: 10, blocks: 1, [lookup-tree-index]
...
lookup-tree-index is an embedded data structure that allows distributed random access
Upcoming version: Hyperdrive 10
Andrew Osheroff (andrewosh)
Mathias Buus (mafintosh)
Andrew Osheroff (andrewosh)
Mathias Buus (mafintosh)
Problem:
Writing tons of files to a folder slows down lookups
The current tree index does a linear scan of a folder to find an entry
Solution:
In Hyperdrive 10 we moved to a hash trie (HAMT)
Find any entry in log4(metadata.length) lookups
HAMT crash course. We want to support:
drive.get('/file/path/somewhere') -> entry
We only append only storage, so we can do:
seq = core.append(data)
data = core.get(seq)
append({ name: '/file/path/somewhere' })
trie_path('/file/path/somewhere') ->
base4_hash('file') + base4_hash('path') + base4_hash('somewhere')
trie_path('/file/path/somewhere') ->
'30103132' + '13200133' + '22330001' .
trie_path('/file/path/somewhere') ->
'301031321320013322330001' .
Write pointers to each latest core entry that shares a prefix off your trie path
#42 .
3
0
1
0
3
1
3
2
1
3
2
0
0
1
3
3
2
2
3
3
0
0
0
1
#42 #39 .
3 3
0 0
1 1
0 0
3 3
1 2
3 0
2 0
1 3
3 3
2 2
0 0
0 1
1 2
3 2
3 0
2 3
2 1
3 1
3 3
0 3
0 0
0 1
1 0
#42 #39 .
3 3
0 0
1 1
0 0
3 3
1 2
3 0
2 0
1 3
3 3
2 2
0 0
0 1
1 2
3 2
3 0
2 3
2 1
3 1
3 3
0 3
0 0
0 1
1 0
#42 .
3
0
1
0
3 ---+
1 2 (#39)
3
2
1
3
2
0
0
1
3
3
2
2
3
3
0
0
0
1
#42 .
3 ---+
0 1 (#35)
1
0
3 ---+
1 2 (#39)
3
2
1
3
2
0
0
1
3
3
2
2
3
3
0
0
0
1
#42 .
3 ---+
0 1 (#35)
1 (max 3 branches)
0
3 ---+
1 2 (#39)
3
2
1
3
2
0
0
1
3
3
2
2
3
3
0
0
0
1
The lookup-tree-index contains only the branch pointers
lookup-tree-index:
[
[ 1, 1, 35 ] // branches at index 1, into 1 (seq #35)
[ 4, 2, 39 ] // branches at index 4, into 2 (seq #39)
]
To find a file path do:
target_trie_path = trie_path('/path/we/want')
head = core.get(core.length - 1)
lookup (head) ->
prefix = calc_shared_prefix(head.trie_path, target_trie_path)
if (prefix == head.trie_path)
return head
if (is_branch(head, prefix))
return lookup(get_branch_node(head, prefix))
return 404
lookup (head) ->
prefix = calc_shared_prefix(head.trie_path, target_trie_path)
if (prefix == head.trie_path)
return head
if (is_branch(head, prefix))
return lookup(get_branch_node(head, prefix))
return 404
lookup (head) ->
prefix = calc_shared_prefix(head.trie_path, target_trie_path)
if (prefix == head.trie_path)
return head
if (is_branch(head, prefix))
return lookup(get_branch_node(head, prefix))
return 404
lookup (head) ->
prefix = calc_shared_prefix(head.trie_path, target_trie_path)
if (prefix == head.trie_path)
return head
if (is_branch(head, prefix))
return lookup(get_branch_node(head, prefix))
return 404
Status:
Fully working, tested, and implemented 😎
Problem: Collaborating with and consuming multiple Hyperdrives is hard
(Multiwriter)
Solution: Hyperdrive 10 adds mount support
Mounts mean you can mount other Hyperdrives inside your own Hyperdrive
drive.mount('/some/path', 'dat://deadbeef...')
Mounts are persisted inside the metadata in a scalable way
Mounted drives can symlink files outside their own drive*
Much simpler and more flexible design that our previous endevours
drive.mount('/some/path', 'dat://deadbeef...')
drive.get('/some/path/and/stuff')
Status:
~Working, implemented, but needs testing 👍
We plan on adding support for "overlapping" mounts as well, but in later releases
Hyperdrive is a POSIX* FS
Hyperdrive is almost a POSIX FS
Problem:
We do not handle random-access writes (RAW) efficiently.
fd = drive.open('/hello/world.txt', 'w')
drive.write(fd, 0, 'hello')
drive.write(fd, 10000, 'world')
Although a bit exotic RAW are important for completeness
Given good RAW support we can run apps like SQL, MongoDB on top of Hyperdrive!
All versioned and in real time
Solution:
Add an inode inspired data structure
Today a file metadata entry just has a contentPointer and length
| Content Hypercore
+ -----------------
| block #0
| block #1
contentPtr -> | block #2
| block #3
| ...
length=40 | block #42
Moves Hyperdrive to a 3 Hypercore structure
1 metadata hypercore
1 inode hypercore
1 content hypercore
Reuses the trie structure above
Instead of storing hashes it uses an incrementing number in base4
alloc_inode() ->
'0000000000000000'
alloc_inode() ->
'0000000000000001'
...
...
alloc_inode() ->
'0000000000000033'
alloc_inode() ->
'0000000000000100'
In the value of the inode we store an interval tree of writes
inode -> {
writes: [
{ bytes: [10, 100], contentPtr: 42, length: 10 },
{ bytes: [1000, 1001], contentPtr: 1, length: 1 },
...
]
}
When the inode gets too big we split it
# pick a global max file size, fx 1 exa byte
put (path, entry, max_size) ->
inode = get(path, entry)
if (inode_size >= max_inode_size) {
remove(path)
put(path + '0', entries < max_size / 2, max_size / 2)
put(path + '1', entries >= max_size / 2, max_size / 2)
} else {
inode.push(entry)
insert_into_trie(path, inode)
}
}
# pick a global max file size, fx 1 exa byte
put (path, entry, max_size) ->
inode = get(path, entry)
if (inode_size >= max_inode_size) {
remove(path)
put(path + '0', entries < max_size / 2, max_size / 2)
put(path + '1', entries >= max_size / 2, max_size / 2)
} else {
inode.push(entry)
insert_into_trie(path, inode)
}
}
# pick a global max file size, fx 1 exa byte
put (path, entry, max_size) ->
inode = get(path, entry)
if (inode_size >= max_inode_size) {
remove(path)
put(path + '0', entries < max_size / 2, max_size / 2)
put(path + '1', entries >= max_size / 2, max_size / 2)
} else {
inode.push(entry)
insert_into_trie(path, inode)
}
}
byte_offset_path (byte_offset, max_size) ->
if (max_size <= 1) return ''
next_bucket = byte_offset < max_size / 2
? '0'
: '1'
return next_bucket
+ byte_offset_path(byte_offset, max_size / 2)
inode = trie_path(inode_num)
+ byte_offset_path(offset, 1 exa byte) .
inode = trie_path(1)
+ byte_offset_path(0, 1 exa byte) .
inode = '0000000000000001' .
+ '000...000'
Stop trie traversal as soon as you hit an inode that has your interval
Status:
Design state (will be a 10.1 feature)
FUSE support
We wanna make Hyperdrive be mountable as an actual FS
FUSE UX in Node.js is ... not great
Download a peer dependency online, very dev-y.
Thank you


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Training programs for any distance and for any level. Workouts can include interval training, tempo runs and long runs. Individualized workouts based on runners goals.
Strength training programs for any level. If you are looking to get faster, stronger or just get started. We have programs to help you reach your goals.
Customized meal plans and nutritional help for any level of athlete. The right nutrition plan designed for you. Let me help you be the best you can be.
Distance running is in Erica’s blood. Her dad was a successful marathoner, winning the Smoky Mountain Marathon in 1982. Erica followed her passion and began running in high school and then was able to continue collegiately at the University of Central Florida, where she was part of the Conference USA Championship winning team for the first time in school history. Erica continued to run after college, shifting from the 5000m to longer distance events. In time, Erica established herself as one of the top female runners in the state of Florida.
Erica ran her first marathon one year after college and ended up with a second place finish and respectable time of 3:05. Erica competed in the Boston Marathon from (2012-2014) with a PR of 2:56. Erica realized at the end of 2014 that she wanted to be more competitive and began training for the marathon with consistent training and coaching. Erica smashed her PR by almost 9 minutes with a 2:47 at the Grandma's Marathon in June 2015. She then went on to become the 2017 Napa Valley Marathon champion, running 2:48:48 on a cold and blustery day.
Erica worked in the corporate world for 3 years in Retail Management and Recruiting until she realized something was missing. She decided to follow her true passion for the sport of running and become a coach.
Erica began as an Assistant Coach for the Winter Park Wildcats, where she motivated and trained with the athletes who went on to become some of the top contenders for the state of Florida. Erica brought that experience to her first Head Coach position at Lake Howell High School, located in Winter Park, Florida, where she was in charge of the Boys and Girls Cross Country and Track and Field teams. She brought several athletes to the State Championships in Cross Country, some of whom went on to compete in college at the NCAA Division I and II levels.
Follow Erica on social media for running tips and to stay inspired. Contact through email and let her help you start your journey to reach your goals. You are not alone. Join us and lets run!


China launches high-res satellite able to provide stereo imagery - CCTV
BEIJING: China launched a new high-resolution remote sensing satellite capable of providing stereoscopic imagery on Sunday, state-run media said, marking another important step as Beijing seeks to reduce reliance on foreign technology in topographic mapping.
A rocket lifted off from the Taiyuan Satellite Launch Centre in northern China at 11:22 a.m. local time, carrying the Gaofen-7 high resolution satellite into its designated orbit, the state-run China Central Television (CCTV) said, citing China National Space Administration.
Gaofen-7, a sub-metre resolution optical satellite, boasts the highest mapping accuracy among its domestic peers, and is able to map China and even the world's lands stereoscopically with a margin of error of less than a metre, according to CCTV.
The satellite, which will be used for land surveys, urban planning and statistical investigation, will help end China's reliance on foreign imports in high-resolution stereo mapping, the CCTV added.
The purpose of China's satellite remote sensing project - the Gaofen series - is to help build an all-weather, 24-hour, global Earth remote sensing system by 2020 capable of monitoring the ground, atmosphere, and oceans, according to CNSA.
The Gaofen project, one of the 16 major projects that are key to the nation's scientific development, is also aimed at comprehensively boosting China's self-reliance in accessing high-resolution earth observation data.
(Reporting by Roxannie Liu, Stella Qiu and Ryan Woo; Editing by Simon Cameron-Moore)


Eric Dier 'very happy' that Jose Mourinho has replaced Mauricio Pochettino
Tottenham midfielder Eric Dier says he is delighted to be working under Jose Mourinho after the Portuguese coach replaced Mauricio Pochettino in charge of the north London club.
Spurs had made a worrying start to the season, sitting in 14th place – 11 points adrift of the top four – when Pochettino was dismissed on Tuesday evening, with Mourinho swiftly announced as his successor the following morning.
Mourinho had just three training sessions before making his bow as Tottenham manager on Saturday, securing a 3-2 victory away to West Ham – their first away win in the Premier League since January.
One of his most notable changes was moving Dier back into the midfield to sit and protect the defence and the side gave an assured display for the first hour before conceding twice late on.
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While Dier was full of praise for Pochettino – who gave him his debut and moulded him into a midfielder – if someone had to replace the Argentine he’s glad it was three-time Premier League title winner Mourinho.
‘I’m very happy that it’s him [Mourinho]. Obviously with my connection to Portugal when I was younger,’ Dier, who came through the academy at Sporting Lisbon, told ESPN after beating West Ham.
‘I grew up watching him coach great teams and winning the biggest titles in football. For me, it’s very special to be able to play for him.
‘We’re very lucky to have played for Pochettino and now for Mourinho. I feel very lucky to be able to have played for these two managers.’
Dier had only started two Premier League matches before Mourinho’s arrival, the first in midfield in October’s 3-0 defeat to Brighton and the second at centre-back in Pochettino’s final game, a 1-1 draw with Sheffield United.
Mourinho was praised for his back-to-basics approach against West Ham, reverting to a 4-2-3-1 formation that saw Spurs’ front three all get on the scoresheet while Dele Alli, playing as a No.10, looked back to his best.
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{% load static %}
Gym Management
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Enquiry
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{% endif %}
{% block body %} {% endblock %} {% block footer%} {% endblock %}


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HMAC Interface
Hash-based Message Authentication Code support.
The update method is used to add data to the message. The digest method is used to extract the message authentication code.
Hash-based Message Authentication Code support.
The update method is used to add data to the message. The digest method is used to extract the message authentication code.


March 2020
Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. Visit WHO Website Watch Video * Video is an external youtube link, which we do not own the contents.
What you should do? Keep clean, wash your hands and isolate yourself.But yes personal hygiene is an everyday thing. What you can do?
Meghalaya Dev Community
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{% extends 'flask_user/public_base.html' %} {% block content %} {% from "base_templates/form_macros.html" import render_submit_field %}
You have not yet created any packages. Create a package in the Playground package area first.
{%- endif %} {% endblock %}


- SequencePointerPullStreamer - Class in bacci.giovanni.bio.sequencing
-
- SequencePointerPullStreamer() - Constructor for class bacci.giovanni.bio.sequencing.SequencePointerPullStreamer
-
- SequencePullStreamer - Interface in bacci.giovanni.bio.sequencing
-
Object that work on sequences.
- SequencePullStreamerDecorator - Class in bacci.giovanni.bio.sequencing
-
- SequencePullStreamerDecorator(SequencePullStreamer) - Constructor for class bacci.giovanni.bio.sequencing.SequencePullStreamerDecorator
-
Standard constructor for decorator pattern.
- SequenceReader - Interface in bacci.giovanni.bio.sequencing
-
An Object that reads sequences as String.
- SequenceStreamer - Interface in bacci.giovanni.bio.sequencing
-
Object that streams a series of sequences one by one.
- SequenceWriter - Interface in bacci.giovanni.bio.sequencing
-
Object able to write a sequence in a file given the id and the sequence.
- setRaf(RandomAccessFile) - Method in class bacci.giovanni.bio.sequencing.RandomFastaSequenceStreamer
-
- setTag(String) - Method in class bacci.giovanni.bio.sequencing.RandomFastaSequenceStreamer
-
This method set a tag associated with the stream
- setTags(Set<String>) - Method in class bacci.giovanni.bio.sequencing.FrequencyTableWriter
-
- SimpleSequencePullStreamer - Class in bacci.giovanni.bio.sequencing
-
- SimpleSequencePullStreamer() - Constructor for class bacci.giovanni.bio.sequencing.SimpleSequencePullStreamer
-
- size() - Method in class bacci.giovanni.bio.sequencing.FilePointerContainer
-
- stream() - Method in class bacci.giovanni.bio.sequencing.RandomFastaSequenceStreamer
-
- stream() - Method in interface bacci.giovanni.bio.sequencing.SequenceStreamer
-
Streams all the sequences in a file or in a list of sequences.
- suite() - Static method in class bacci.giovanni.bio.sequencing.AppTest
-