About
- Do I need to log in to play?
- No. Submitted solutions will be executed and checked without logging in, but nothing will be saved and you won't appear on the leaderboards.
- Is code.golf free as in speech?
- Yes! It is written in Go, licensed under MIT, and available on GitHub; patches welcome!
- Is code.golf free as in beer?
-
Yes! However hosting isn't, so if you enjoy Code Golf consider donating to its continued existence and improvement via GitHub Sponsors.
Sponsors will receive a couple of additional perks, with more planned:
* Badge hidden if sponsorship is anonymous. Free Sponsor Profile Badge No Yes * Follow Limit {{ .Data.FollowLimit }} {{ .Data.FollowLimitSponsor }} - What languages are supported?
-
{{ range .Data.Langs }}
{{ end }}{{ svg .ID }} {{ .Name }}
{{ .Size }}{{ .Version }}{{ trimPrefix (trimPrefix .Website "https://") "www." }}If you'd like to see another language added then raise an issue.
- Are warnings ignored?
- Yes. Only STDOUT is checked against the solution, STDERR is however shown back to you to ease debugging.
- How are arguments passed to my program?
- Some holes pass arguments, for those your program should read them from the command line arguments (ARGV).
- How do I print Unicode characters?
-
Your source code is sent to the interpreter encoded in UTF-8, and is expected to write output encoded in UTF-8 to STDOUT. For languages where it matters, your code is run in the
en_US
locale with a UTF-8 output encoding.In Unicode-aware languages like Python, this means
print("🙂")
andprint(chr(0x1f642))
both produce the emoji U+1F642 "Slightly Smiling Face" 🙂, which is encoded asf0 9f 99 82
in UTF-8.In less Unicode-aware languages where strings are byte strings, you might still get away with UTF-8 in string literals. For example, OCaml treats
"🙂"
as a string of length 4 (four bytes), butChar.chr 0x1f642
is an error.In yet other languages, like brainfuck, you have to print the individual bytes
f0 9f 99 82
one by one. - How are solutions ranked?
- There are two scorings in use, bytes and chars. Bytes is the number of bytes of a solution encoded in UTF-8. Chars is the number of Unicode codepoints of a solution. Users may submit up to two solutions per hole per language. This is handled automatically when you enter two solutions and each minimizes a different scoring. Each scoring has its own set of leaderboards. For the chars scoring, both “A” (U+0041 Latin Capital Letter A) and “😉” (U+1F609 Winking Face) cost the same despite the 1:4 ratio in byte count in UTF-8.
- How are solutions scored?
-
For each hole, in each language, up to 1,000 points are available.
The formula is:
Points = Sb ÷ Su × 1000
Where Su is the length of the user's solution, and Sb is a Bayesian estimator of the form:Sb = ((√n + 2) ÷ (√n + 3)) × S + (1 ÷ (√n + 3)) × Sa
n is the number of solutions in this hole in this language. S is the length of the shortest solution in this hole in this language. Sa is the shortest solution among all languages in this hole.The confidence interval is chosen such that the shortest solution in any language will receive a score of at least 750.
Your overall score is the sum of your highest points in each hole.
- Are submissions resource-constrained?
- Yes. Execution time is limited to 5 seconds, CPU & RAM usage is unbounded but this will probably change soon. The length of each solution must be lower than 128 KiB as that's the maximum length of any program argument under Linux.
- Is there an API?
- An API is currently being developed and documented in standard OpenAPI format. A legacy API that just returns scores in JSON exists. The URL is /scores/all-holes/all-langs/all. Note that all-holes and all-langs may be replaced by the names of specific holes and languages.
- Can I see other people's solutions?
- No, that way all holes stay competitive. If you're stuck on how to shave a couple of strokes off your solution then why not visit our Discord server for some advice.


DEMO: Fluid Width Video
CSS
For cases where we use HTML5 video directly through <video> tags.
jQuery JavaScript
For everything else...
Examples
All these videos use code just straight up copy-and-pasted from their source.
For cases where we use HTML5 video directly through <video> tags.
For everything else...
All these videos use code just straight up copy-and-pasted from their source.


Watch how a Tesla in autopilot mode avoids plowing into a group of ducks crossing the road
by Mark FrauenfelderThis seems like some impressive trolley problem decision-making.
Image: YouTube
This seems like some impressive trolley problem decision-making.
Image: YouTube


|
Guild Wars 2 - Elite Insights
2.25+
JSON doc
|
Support stats More...
Properties | |
| long | Resurrects [get] |
| Number of time ressurected someone More... | |
| double | ResurrectTime [get] |
| Time passed on ressurecting More... | |
| long | CondiCleanse [get] |
| Number of time a condition was removed, self excluded More... | |
| double | CondiCleanseTime [get] |
| Total time of condition removed, self excluded More... | |
| long | CondiCleanseSelf [get] |
| Number of time a condition was removed from self More... | |
| double | CondiCleanseTimeSelf [get] |
| Total time of condition removed from self More... | |
| long | BoonStrips [get] |
| Number of time a boon was removed More... | |
| double | BoonStripsTime [get] |
| Total time of boons removed from self More... | |
Support stats
Definition at line 302 of file JsonStatistics.cs.
|
get |
Number of time a boon was removed
Definition at line 331 of file JsonStatistics.cs.
|
get |
Total time of boons removed from self
Definition at line 335 of file JsonStatistics.cs.
|
get |
Number of time a condition was removed, self excluded
Definition at line 315 of file JsonStatistics.cs.
|
get |
Number of time a condition was removed from self
Definition at line 323 of file JsonStatistics.cs.
|
get |
Total time of condition removed, self excluded
Definition at line 319 of file JsonStatistics.cs.
|
get |
Total time of condition removed from self
Definition at line 327 of file JsonStatistics.cs.
|
get |
Number of time ressurected someone
Definition at line 307 of file JsonStatistics.cs.
|
get |
Time passed on ressurecting
Definition at line 311 of file JsonStatistics.cs.


Books that Surprise you!
Search:
Click here to search your book!
Buy
Like
Dislike
Book
Likes
Dislikes
Total de Likes:
Total de Dislikes:


importance: 5
Create a constructor function Accumulator(startingValue)
.
Object that it creates should:
value
. The starting value is set to the argument of the constructor startingValue
.read()
method should use prompt
to read a new number and add it to value
.In other words, the value
property is the sum of all user-entered values with the initial value startingValue
.
Here’s the demo of the code:
let accumulator = new Accumulator(1); // initial value 1
accumulator.read(); // adds the user-entered value
accumulator.read(); // adds the user-entered value
alert(accumulator.value); // shows the sum of these values
[demo]


Hello @user.firstName,
you tried to register an account with yetu.me, but there is already an account registered with your email address. If you don‘t remember your password please follow
this link
to reset it Thank you!
© 2015 yetu AG • Version 1.0
Privacy Policy
-
Terms and Conditions
-
Imprint


|
Embedded Artistry Framework
Embedded Systems C++ Framework
|
#include <ctype.h>
Functions | |
| int | isspace (int c) |
| Checks if the given character is a whitespace character. More... | |
| int isspace | ( | int | ch | ) |
Checks if the given character is a whitespace character.
Checks if the given character is a whitespace character. The following are examples of a whitespace character:
The behavior is undefined if the value of ch is not representable as unsigned char and is not equal to EOF.
| ch | The character to classify |
Referenced by atol(), atoll(), bad_input(), good_input(), strtol(), strtoll(), strtoul(), and strtoull().


|
Juan Antonio Breña Moral (40) Chapter Lead, Triathlete, Deejay & Mindstorms/STEAM teacher. Twitter | Github | Linkedin |
|
| Purpose: "Riding dragons with innovation & fun" | |
|
Quote: "Pressure makes diamonds." - George S. Patton Jr. |
Average Java developer, implements in many cases, Microservices using an imperative style.
In Java, you can develop software using multiple paradigms with success.
This talk tries to introduce some functional concepts and patterns with the help of VAVR.
A strong type system can not only prevent errors, but also guide you and provide feedback in your design process.
Using functional programming language changes the way you write code. You start thinking about the (data) types that represent the data your code works with.
Function composition is an act or mechanism to combine simple functions to build more complicated ones.
Programmers frequently apply functions to results of other functions, and almost all programming languages allow it.
The ability to easily compose functions encourages factoring (breaking apart) functions for maintainability and code reuse.
In Java 8, the language was released with some Types to increase the expressiveness, like Optional to model optionality in the value returned by a method.
Following the same idea, how to model the Exceptionality? Unfortunately, the language doesn't provide any solution in the line of Optional.
How to solve the gap in Java? VAVR
| Imperative | Control flow is explicit. Commands show how the computation takes place, step by step. |
| Declarative | Control flow is implicit. the developer states only what the results should look like, not how to obtain it. |
| Functional | Control flow is expressed by combining function calls rather than by assigning values to variables. |
| Object-oriented | Computation is affected by sending messages to objects. Objects has state and behaviour. |
| Event-driven | Control flow is determined by asynchronous actions. |
| Concept | Description |
|---|---|
| Lazy | Refers to the delaying of the evaluation of an operation until it is needed. Lazy evaluation support infinite streams. |
| Eager | An operation is executed as soon as it is encountered. |
Referential transparency is generally defined as the fact that an expression, in a program, may be replaced by its value (or anything having the same value) without changing the result of the program.
Type class: defines a set of functions (usually only the signature, with no concrete implementation, although a default implementation can be provided).
ADT: A very specific composite type created from a set of simpler constructs which together define the different states or structures of the ADT.
A closure is the combination of a function bundled together (enclosed) with references to its surrounding state (the lexical environment). In other words, a closure gives you access to an outer function’s scope from an inner function.
Closures are commonly used to give objects data privacy. When you use closures for data privacy, the enclosed variables are only in scope within the containing (outer) function.
VAVR is a functional library for Java 8+ that provides persistent data types and functional control structures.
Algebraic Data Types, or ADTs for short, are powerful in representing the data model in a concise and transparent way.
Sum types can be seen as types that capture state. technically, they are formed by a sealed base interface and two or more final implementations.
Product types are different, they cannot have different states. The values can be seen as indexed values.
Option is a monadic container type which represents an optional value. Instances of Option are either an instance of Option.Some or the singleton Option.None.
JavadocsEither represents a value of two possible types. An Either is either a Either.Left or a Either.Right.
By convention, the Left signifies a failure case result and the Right signifies a success.
JavadocsThe Try control gives us the ability write safe code without focusing on try-catch blocks in the presence of exceptions.
JavadocsFunctional programming is all about values and transformation of values using functions. Java 8 just provides a Function which accepts one parameter and a BiFunction which accepts two parameters. Vavr provides functions up to a limit of 8 parameters. The functional interfaces are of called Function0, Function1, Function2, Function3 and so on. If you need a function which throws a checked exception you can use CheckedFunction1, CheckedFunction2 and so on.
In fact Vavr functional interfaces are Java 8 functional interfaces on steroids. They also provide features like:
You can compose functions. In mathematics, function composition is the application of one function to the result of another to produce a third function. For instance, the functions f : X → Y and g : Y → Z can be composed to yield a function h: g(f(x)) which maps X → Z.
The term partial function comes from mathematics. A partial function from X to Y is a function f: X′ → Y, for some subset X′ of X. It generalizes the concept of a function f: X → Y by not forcing f to map every element of X to an element of Y. That means a partial function works properly only for some input values.
Partial application allows you to derive a new function from an existing one by fixing some values. You can fix one or more parameters, and the number of fixed parameters defines the arity of the new function such that new arity = (original arity - fixed parameters). The parameters are bound from left to right.
Currying is a technique to partially apply a function by fixing a value for one of the parameters, resulting in a Function1 function that returns a Function1. When a Function2 is curried, the result is indistinguishable from the partial application of a Function2 because both result in a 1-arity function.
Memoization is a form of caching. A memoized function executes only once and then returns the result from a cache.
Property-based testing
A live example based on WebMvc.fn to ilustrate some examples.


Welcome(ish) to the future home of my My LinkedIn Resume, My GitHub DemoCode and My Art Shop. this is an area of content, perhaps a lorem ipsum paragraph would be nice. for instance some text about yourself and how you want this to eventually be a store front and resume for your work.Also this is read in third person! YAY!
THEATRE OF THE ABSURD!!!
Make sure to eat your eggs and bacon!


Segregated Temporal Assembly Recurrent Networks for Weakly Supervised Multiple Action Detection
Abstract
Video text spotting is still an important research topic due to its various real-applications. This paper proposes a segregated temporal assembly recurrent (STAR) network for weakly-supervised multiple action detection. The model learns from untrimmed videos with only supervision of video-level labels and makes prediction of intervals of multiple actions. Speciﬁcally, we ﬁrst assemble video clips according to class labels by an attention mechanism that learns class-variable attention weights and thus helps the noise relieving from background or other actions. Secondly, we build temporal relationship between actions by feeding the assembled features into an enhanced recurrent neural network. Finally, we transform the output of recurrent neural network into the corresponding action distribution. In order to generate more precise temporal proposals, we design a score term called segregated temporal gradient-weighted class activation mapping (ST-GradCAM) fused with attention weights. Experiments on THUMOS’14 and ActivityNet1.3 datasets show that our approach outperforms the state-of-the-art weakly-supervised method, and performs at par with the fully-supervised counterparts. [Paper]Highlights Contributions
❃ We reformulate the multiple action detection from a multi-instance multi-label (MIML) perspective, i.e., extracting instance-patterns and generating action labels, which eliminates interference among unrelated action features and captures temporal dependency between multiple concurrent actions.
❃ We propose an end-to-end framework called Segregated Temporal Assembly Recurrent (STAR), which includes a well-designed attention module and an enhanced RNN, is developed to be trained in a weakly supervised manner from videos with only video-level labels.
❃ We design an ST-GradCAM operation fused with class-variable assembly weights for action temporal localization.
❃ Our extensive experiments demonstrate that our weakly supervised framework achieves impressive performance on the challenging THUMOS'14 and ActivityNet1.3 datasets for action detection, comparable with those of supervised learning methods.
Recommended Citations
If you find our work is helpful to your research, please feel free to cite us:@inproceedings{xu2019segregated, title={Segregated temporal assembly recurrent networks for weakly supervised multiple action detection}, author={Xu, Yunlu and Zhang, Chengwei and Cheng, Zhanzhan and Xie, Jianwen and Niu, Yi and Pu, Shiliang and Wu, Fei}, booktitle={Proceedings of the AAAI Conference on Artificial Intelligence}, volume={33}, pages={9070--9078}, year={2019} }


This inspection reports when any else if statements are used with a reversible expression, example 'if(i != 71) { } else { }'


{% extends "base.html" %} {% block content %} {{ form.media }}
The information below contains values from previous years or other sources which you can copy paste:


Backup actions performed: db backup, website files backup, zipped, saved into <~backup_dir> (kept <~keep_days> days), stored to S3 (kept <~keep_days_S3> days)
<~backup_dir>
Download latest backup <~latest_backup>, <~latest_backup_size>


Cyborg, a club under the Technical Society, National Institute of Technology, Rourkela focusses on Robotics and Automation. It has various captivating undertakings thriving under it. It conducts various classes and workshops. One of them is the IGVC(Intelligent Ground Vehicle Competition). The competition has taken place each year since 1993. IGVC is an internationally acclaimed competition that aims to integrate control theory, computer vision, vehicular electronics fundamentals to design and build an unmanned autonomous system. The major Objective involves following a path of obstacles dodging them to reach the final point. Team Cyborg aspires to show its mettle in the internationally acclaimed event like IGVC and display a commendable performance in it.
Cyborg, a club under the Technical Society, National Institute of Technology, Rourkela focusses on Robotics and Automation. It works under the joint effort of four essential domains - Mechanics, Electronics, Programming and Image Processing. It has various captivating undertakings thriving under it. It conducts various classes and workshops. It has been a regular competitor of the highest levels in various fests and competitions winning various laurels in top-notch competitions. Inquisitive talents of this institute have been getting the opportunity of stepping into our forum where we nurture and display the store of our white and grey matter, BECAUSE WE DESIGN INTELLIGENCE.
The main aim of the software subsystem is to develop modular and error-free software, to get the vehicles working for challenging tasks.
The main aim of the electronics subsystem is to make the bridge between the software stack of the vehicle and the outside world.
The goal of the mechanical subsystem is to sketch a modular and portable design in Solidworks and deliver full potential to all the components of the vehicle.
Good tools make website development quicker and easier to maintain than if you did everything by hand.
#UnravellingTech #Explorer
Commitment to ensuring the team succeeds with all tasks, duties, and projects .
Our life is what our thoughts make it.
Team player... Nothing without my team


| 025:032 Khan | : |
And those who disbelieve say: "Why is not the Qur'an revealed to him all at once?" Thus (it is sent down in parts), that We may strengthen your heart thereby. And We have revealed it to you gradually, in stages. (It was revealed to the Prophet SAW in 23 years.). |
| 025:032 Maulana | : |
And those who disbelieve say: Why has not the Qur'an been revealed to him all at once? Thus, that We may strengthen thy heart thereby and We have arranged it well in arranging. |
| 025:032 Pickthal | : |
And those who disbelieve say: Why is the Qur'an not revealed unto him all at once? (It is revealed) thus that We may strengthen thy heart therewith; and We have arranged it in right order. |
| 025:032 Rashad | : |
Those who disbelieved said, "Why did not the Quran come through him all at once?" We have released it to you gradually, in order to fix it in your memory. We have recited it in a specific sequence. |
| 025:032 Sarwar | : |
The unbelievers have said, "Why was the whole Quran not revealed to him at once?" We have revealed it to you in gradual steps to strengthen your hearts and give you explanations. |
| 025:032 Shakir | : |
And those who disbelieve say: Why has not the Quran been revealed to him all at once? Thus, that We may strengthen your heart by it and We have arranged it well in arranging. |
| 025:032 Sherali | : |
And those who disbelieve say, 'Why was not the Qur'an revealed to him all at once?' WE have revealed it in this manner that WE may strengthen thy heart therewith. And WE have arranged it in the Best form. |
| 025:032 Yusufali | : |
Those who reject Faith say: "Why is not the Qur'an revealed to him all at once? Thus (is it revealed), that We may strengthen thy heart thereby, and We have rehearsed it to thee in slow, well-arranged stages, gradually. |
Concepts/Themes Covered: أركان الإسلام > (محمد (صلى الله عليه وسلم > مخاطبة الله إياه Pillars of Islam> The Blessed Muhammad(PBUH)> Allah's Address to Him(PBUH) الإيمان > الغيب > الغيب النفسي > الفؤاد Faith> The Unseen World> The Psychological Secret> The Heart القرآن الكريم > حقيقته وتصديقه للكتب الأوائل The Holy Quran> The Quran's Reality and its confirmation of the Previous Books
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


package core
Ordering
- Alphabetic
Visibility
- Public
- All
Type Members
- class ApplicationStore extends Listener
- class Guitar extends AnyRef
- trait Listener extends AnyRef
-
class
LoopAccessor
[T] extends AnyRef
Wraps a Sequence to access at an infinite index level, if the index you requested is passed the size of the sequence, it loops back to the beginning
-
case class
SelectedNote
(note: String, string: Int, fret: Int) extends Product with Serializable
SelectedNote represents a note that the user has selected These notes are highlighted on the UI
Value Members
- object ApplicationStore
- object ClearSelectedNotes extends Product with Serializable
- object Guitar
- object LoopAccessor
-
object
NoteMapper
Utilities for mapping notes
- object StringMapper


☰
Experimental Design
Analysis of Variance (ANOVA)
Load data
Help
Settings
Analysis
Data
Cornfield-Tukey Rules
Homoscedasticity
Post Hoc
Tests
Averages


| Below output is produced from AngularJS's {{}} directive. |
|
{{item.Name}} is in our stock. |
| Below output is produced from AngularJS's ng-bind directive. |
|
is in our stock. |
| Below output is produced from AngularJS's ng-non-bindable directive. |
|
{{item.Name}} is in our stock. |


Tengen Go 2010 Zombie Process Independent Game Development Project
An ancient Chinese board game for the iPad. Allows two players to play head to head, or play online using an existing popular server. I developed this application without assistance over the course of about a month and a half, and have continued to enhance it since. Network communication uses Telnet, with a human readable protocol for player matchmaking and game logic.
The game board and pieces are rendered using Core Graphics. In particular, the white game pieces, traditionally made of clam shell, use masking and a pattern texture so that each one is slightly different. Core Animation is also used extensively for the game pieces and other effects. User interaction is modeled as closely as possible to a real game set.
This app required the use of a wide variety of user interface elements, including searching/filtering, custom table cells, table sorting, gesture recognizers, popover and modal views, pickers, and pervasive use of auto-rotation. It is localized in three languages. Due to the complexity of the server protocol, it has been a challenge to present every possible control state to the user in an intuitive fashion. As a result, a large percentage of the interface is dynamic, with controls changing depending on the commands available from the server.
I have enjoyed fair success with this product, and have continued to add features occasionally.


Time window
The number of bins is fixed at 100.
For most of the following shortcuts, if you use alt-x instead of just x you will toggle the settings pane for the given feature, e.g. alt-r toggles the settings pane for spatial ratemap, whereas r by itself toggles the actual ratemap plots. (In some cases alt is not needed because toggling the pane is the only action linked to the shortcut.)
Shorcuts for the plots shown for each group; use shift to select multiple:
Shorcuts applied to the "active" group, which is the one under the cursor:
Other shortcuts:
Bin size
Upper limit
Histogram shows slowest speed at the top, and upper limit speed at the bottom.
WARNING: speed plots are still in alpha testing (unlike everything else which is just about in beta!).
There are two palette modes.
Render in
In "flag" mode, each group from 0 to 30 gets its own color, and is rendered solidly in that color.
In "density" mode, the color of each pixel reflects the proportion of the waves which cross it.
Bin size
Smoothing kernel boxcar
Ratemap frequency values are
The true peak rate is shown as "spa max" when you move your cursor over a group.
Ratemap height:
Bin size
Smoothing kernel boxcar
Note that in 2-spot LED mode, the relative position of the two LEDs is used, in 1-spot LED mode, the direction of movement is used (this is sometimes refered to as "displacement direction").
The true peak rate is shown as "dir max" when you move your cursor over a group.
If you tracked with two LEDs you can read in both data streams. "Standard" post-processing will be applied to decide when the two spots have swapped.
Use
The following filtering and smoothing is applied to the one/two data streams separately. If two LEDs are used, a single estimate of position is produced at the end, using a weighted sum.
Speed
Where the speed appears to rise above this threshold the data is considered invalid.
Where data is missing (either because no pixels were tracked for the given sample or the above filter invalidated the data), new values are created by linearly interpolated across the gaps.
Smoothing
Following filtering for speed and interpolation, the X and Y data is smoothed using a boxcar (i.e. moving average) of the above width.


Explore JWQL database tables through the web browser
This page allows users to interactively explore the JWQL database tables. The main function of this tool is to
visually inspect data through the JWQL Web Application. Simply select a table from the dropdown menu to begin,
if further investigation of the table is needed, you can click the "Download Data" button to obtain the displayed
table as a CSV file.
The database table naming convention follows:
Importing A Table Using Python's Pandas Library
After downloading JWQLDB data, it is simple to import this data in a python session using pandas. Visit the pandas documentation for more information.
import pandas as pd # import pandas
# Open CSV file with pandas
nircam_bias_stats = pd.read_csv('/path/to/downloads/nircam_bias_stats.csv')
print(nircam_bias_stats.columns) # to see column names.
print(nircam_bias_stats['expstart']) # to print exposure start times.


public class ServerConnection
extends java.lang.Object
| Modifier and Type | Class | Description |
|---|---|---|
protected class |
ServerConnection.ReceiverThread |
Thread used to receive packets from the remote Server.
|
protected class |
ServerConnection.TTPReceiverThread |
Thread used to receive packets from the remote Server.
|
| Modifier and Type | Field | Description |
|---|---|---|
static java.lang.String |
MAC_ALGORITHM |
|
protected java.util.concurrent.LinkedBlockingQueue<byte[]> |
outQueue |
| Constructor | Description |
|---|---|
ServerConnection(ServerViewController controller,
java.net.Socket socket,
int remoteId,
java.util.concurrent.LinkedBlockingQueue<SystemMessage> inQueue,
ServiceReplica replica) |
| Modifier and Type | Method | Description |
|---|---|---|
void |
authenticateAndEstablishAuthKey() |
|
javax.crypto.SecretKey |
getSecretKey() |
|
protected void |
reconnect(java.net.Socket newSocket) |
(Re-)establish connection between peers.
|
void |
send(byte[] data,
boolean useMAC) |
Used to send packets to the remote Server.
|
void |
shutdown() |
Stop message sending and reception.
|
public static final java.lang.String MAC_ALGORITHM
protected java.util.concurrent.LinkedBlockingQueue<byte[]> outQueue
public ServerConnection(ServerViewController controller, java.net.Socket socket, int remoteId, java.util.concurrent.LinkedBlockingQueue<SystemMessage> inQueue, ServiceReplica replica)
public javax.crypto.SecretKey getSecretKey()
public void shutdown()
public final void send(byte[] data, boolean useMAC) throws java.lang.InterruptedException
java.lang.InterruptedException
protected void reconnect(java.net.Socket newSocket)
newSocket
- socket created when this Server accepted the connection
(only used if processId is less than remoteId)public void authenticateAndEstablishAuthKey()
Copyright © 2018. All rights reserved.


We are in block head. {% raw %}{{ hello }}{% endraw %}
In Body! {% raw %}
test
App 2
test
Sound Level
{% endraw %}


Syntax:
copy-alist alist => new-alist
Arguments and Values:
alist---an association list.
new-alist---an association list.
Description:
copy-alist returns a copy of alist.
The list structure of alist is copied, and the elements of alist which are conses are also copied (as conses only). Any other objects which are referred to, whether directly or indirectly, by the alist continue to be shared.
Examples:
(defparameter *alist* (acons 1 "one" (acons 2 "two" '()))) *alist* => ((1 . "one") (2 . "two")) (defparameter *list-copy* (copy-list *alist*)) *list-copy* => ((1 . "one") (2 . "two")) (defparameter *alist-copy* (copy-alist *alist*)) *alist-copy* => ((1 . "one") (2 . "two")) (setf (cdr (assoc 2 *alist-copy*)) "deux") => "deux" *alist-copy* => ((1 . "one") (2 . "deux")) *alist* => ((1 . "one") (2 . "two")) (setf (cdr (assoc 1 *list-copy*)) "uno") => "uno" *list-copy* => ((1 . "uno") (2 . "two")) *alist* => ((1 . "uno") (2 . "two"))
Side Effects: None.
Affected By: None.
Exceptional Situations: None.
See Also:
Notes: None.


The Border Radius
5px
10px
25px
50%
The Opacity Family
RGBa & HSLa
The Box Shadows
The First Box Shadow
The Second Box Shadow
The Third Box Shadow
The Text Shadows
The First Text Shadow
The Second Text Shadow
The Third Text Shadow
The Linear Gradients
The Radial Gradients
The Filter
The Transform
The First Box
The Second Box
The Third Box
The Fourth Box
The Transition
1
2
3
4
The Animation
1
2
3
4


| Fig 1 |
Goodness of fit of the simulated statewide average probabili ... |
| Fig 2 |
Simulated cumulative average water balance of Illinois. Pote ... |
| Fig 3 |
Changes in the number of months in deficit between May and A ... |
| Fig 4 |
Simulated state average probability of a field working day o ... |


| Name |
Description |
|
IMC_Characteristics |
Characteristic curves of Induction machine with squirrel
cage |
|
IMC_DOL |
Induction machine with squirrel cage started directly on line
(DOL) |
|
IMC_YD |
Induction machine with squirrel cage starting Y-D |
|
IMC_Transformer |
Induction machine with squirrel cage starting with
transformer |
|
IMC_Conveyor |
Induction machine with squirrel cage and inverter driving a
conveyor |
|
IMC_withLosses |
Induction machine with squirrel cage and losses |
|
IMC_Initialize |
Steady-state initialization of induction machine with squirrel
cage |
|
IMS_Characteristics |
Characteristic curves of induction machine with slip rings |
|
IMS_Start |
Starting of induction machine with slip rings |


JavaScript is disabled on your browser.
Skip navigation links
Package
Class
Use
Tree
Deprecated
Index
Help
Prev
Next
Frames
No Frames
All Classes
A
C
F
G
I
L
T
V
A
add(double, double, long, T)
- Method in class com.github.davidmoten.geo.mem.
Geomem
Adds a record to the in-memory store with the given position and time.
add(double, double, long, T, R)
- Method in class com.github.davidmoten.geo.mem.
Geomem
* Adds a record to the in-memory store with the given position and time and id.
add(double, double, long, T, Optional<R>)
- Method in class com.github.davidmoten.geo.mem.
Geomem
Adds a record to the in-memory store with the given position and time and id.
add(Info<T, R>)
- Method in class com.github.davidmoten.geo.mem.
Geomem
Adds a record to the in-memory store with the given position, time and id.
C
com.github.davidmoten.geo.mem
- package com.github.davidmoten.geo.mem
F
find(double, double, double, double, long, long)
- Method in class com.github.davidmoten.geo.mem.
Geomem
Returns as an
Iterable
the results of a search within the bounding box given and where start <=time < finish.
G
Geomem
<
T
,
R
> - Class in
com.github.davidmoten.geo.mem
Provides fast concurrent querying using in memory
ConcurrentSkipListMap
s and geohash to store data with time and position.
Geomem()
- Constructor for class com.github.davidmoten.geo.mem.
Geomem
I
id()
- Method in class com.github.davidmoten.geo.mem.
Info
Info
<
T
,
R
> - Class in
com.github.davidmoten.geo.mem
Info(double, double, long, T, Optional<R>)
- Constructor for class com.github.davidmoten.geo.mem.
Info
L
lat()
- Method in class com.github.davidmoten.geo.mem.
Info
lon()
- Method in class com.github.davidmoten.geo.mem.
Info
T
time()
- Method in class com.github.davidmoten.geo.mem.
Info
toString()
- Method in class com.github.davidmoten.geo.mem.
Info
V
value()
- Method in class com.github.davidmoten.geo.mem.
Info
A
C
F
G
I
L
T
V
Skip navigation links
Package
Class
Use
Tree
Deprecated
Index
Help
Prev
Next
Frames
No Frames
All Classes
Copyright © 2013–2021. All rights reserved.


DUP candidate says party will still have 'leverage' over Tories after election
Lagan Valley candidate Sir Jeffrey Donaldson is not convinced that Boris Johnson's Tory Party will have a huge majority after the general election next month
by Tom Davidson, https://www.mirror.co.uk/authors/tom-davidson/The DUP will still have 'leverage' over the Tories after the general election , according to a candidate for the unionist party.
Lagan Valley candidate Sir Jeffrey Donaldson reckons that Boris Johnson will not enjoy a 'huge majority' come December 13.
And that means the Tories could still rely on the DUP to push through crucial votes in parliament - although the party is set against his Brexit deal.
Theresa May's disastrous 2017 election performance meant she was forced to enter into a 'confidence and supply' arrangement with the party which had secured 10 MPs.
And the DUP were able to secure more than £1bn for Northern Ireland as part of the deal.
Speaking as part of a panel of representatives from the five main political parties in Northern Ireland who were invited to address business leaders, Sir Jeffrey said: "I am not convinced Boris Johnson is going to come back with a huge majority and I think that gives us leverage.
"Whilst we can go to Westminster and we can do what we do, I believe it is ultimately much more powerful if there is a collective voice from Northern Ireland saying what we need."
Today Boris Johnson visited a washing machine manufacturer in the north-east of England as he embarks on a series of election campaign visits in the region.
The Prime Minister met workers and tried his hand at moulding parts at the Ebac factory in Newton Aycliffe.
Mr Johnson posed through drums and placed "Made in Britain" stickers on new machines as he toured the factory - the UK's only washing machine manufacturer.


This running key cipher uses a Vigenere type cyper based off of a ROT shift that is pseudo-randomized through the use of 2 keys instead of 1.
Additionally, we can implement any types of "alphabet" or character set for additional complexity, ultimately making this really a 3 key cipher


Note
Click here to download the full example code
What is the opset number?¶
Every library is versioned. scikit-learn may change the implementation of a specific model. That happens for example with the SVC model where the parameter break_ties was added in 0.22. ONNX does also have a version called opset number. Operator ArgMin was added in opset 1 and changed in opset 11, 12, 13. Sometimes, it is updated to extend the list of types it supports, sometimes, it moves a parameter into the input list. The runtime used to deploy the model does not implement a new version, in that case, a model must be converted by usually using the most recent opset supported by the runtime, we call that opset the targeted opset. An ONNX graph only contains one unique opset, every node must be described following the specifications defined by the latest opset below the targeted opset.
This example considers an IsolationForest and digs into opsets.
Data¶
A simple example.
from onnx.defs import onnx_opset_version
from skl2onnx import to_onnx
import numpy
import matplotlib.pyplot as plt
from sklearn.ensemble import IsolationForest
from sklearn.datasets import make_blobs
X, y = make_blobs(n_samples=100, n_features=2)
model = IsolationForest(n_estimators=3)
model.fit(X)
labels = model.predict(X)
fig, ax = plt.subplots(1, 1)
for k in (0, 1):
ax.plot(X[labels == k, 0], X[labels == k, 1], 'o', label="cl%d" % k)
ax.set_title("Sample")
Out:
Text(0.5, 1.0, 'Sample')
ONNX¶
onx = to_onnx(model, X[:1].astype(numpy.float32))
print(onx)
Out:
ir_version: 8
producer_name: "skl2onnx"
producer_version: "1.11"
domain: "ai.onnx"
model_version: 0
doc_string: ""
graph {
node {
input: "X"
input: "node_sample0_Gathercst"
output: "node_sample0_output0"
name: "node_sample0_Gather"
op_type: "Gather"
attribute {
name: "axis"
i: 1
type: INT
}
domain: ""
}
node {
input: "node_sample0_output0"
output: "node_sample0_Y0"
name: "node_sample0_TreeEnsembleRegressor"
op_type: "TreeEnsembleRegressor"
attribute {
name: "n_targets"
i: 1
type: INT
}
attribute {
name: "nodes_falsenodeids"
ints: 38
ints: 35
ints: 18
ints: 5
ints: 0
ints: 11
ints: 10
ints: 9
ints: 0
ints: 0
ints: 0
ints: 15
ints: 14
ints: 0
ints: 0
ints: 17
ints: 0
ints: 0
ints: 20
ints: 0
ints: 28
ints: 25
ints: 24
ints: 0
ints: 0
ints: 27
ints: 0
ints: 0
ints: 32
ints: 31
ints: 0
ints: 0
ints: 34
ints: 0
ints: 0
ints: 37
ints: 0
ints: 0
ints: 70
ints: 45
ints: 42
ints: 0
ints: 44
ints: 0
ints: 0
ints: 57
ints: 52
ints: 51
ints: 50
ints: 0
ints: 0
ints: 0
ints: 56
ints: 55
ints: 0
ints: 0
ints: 0
ints: 63
ints: 60
ints: 0
ints: 62
ints: 0
ints: 0
ints: 67
ints: 66
ints: 0
ints: 0
ints: 69
ints: 0
ints: 0
ints: 74
ints: 73
ints: 0
ints: 0
ints: 0
type: INTS
}
attribute {
name: "nodes_featureids"
ints: 0
ints: 0
ints: 1
ints: 1
ints: 0
ints: 0
ints: 0
ints: 1
ints: 0
ints: 0
ints: 0
ints: 0
ints: 1
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 1
ints: 1
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 1
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 1
ints: 1
ints: 0
ints: 0
ints: 0
ints: 1
ints: 1
ints: 0
ints: 0
ints: 0
ints: 0
ints: 1
ints: 1
ints: 0
ints: 0
ints: 1
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
type: INTS
}
attribute {
name: "nodes_hitrates"
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
type: FLOATS
}
attribute {
name: "nodes_missing_value_tracks_true"
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
type: INTS
}
attribute {
name: "nodes_modes"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "LEAF"
type: STRINGS
}
attribute {
name: "nodes_nodeids"
ints: 0
ints: 1
ints: 2
ints: 3
ints: 4
ints: 5
ints: 6
ints: 7
ints: 8
ints: 9
ints: 10
ints: 11
ints: 12
ints: 13
ints: 14
ints: 15
ints: 16
ints: 17
ints: 18
ints: 19
ints: 20
ints: 21
ints: 22
ints: 23
ints: 24
ints: 25
ints: 26
ints: 27
ints: 28
ints: 29
ints: 30
ints: 31
ints: 32
ints: 33
ints: 34
ints: 35
ints: 36
ints: 37
ints: 38
ints: 39
ints: 40
ints: 41
ints: 42
ints: 43
ints: 44
ints: 45
ints: 46
ints: 47
ints: 48
ints: 49
ints: 50
ints: 51
ints: 52
ints: 53
ints: 54
ints: 55
ints: 56
ints: 57
ints: 58
ints: 59
ints: 60
ints: 61
ints: 62
ints: 63
ints: 64
ints: 65
ints: 66
ints: 67
ints: 68
ints: 69
ints: 70
ints: 71
ints: 72
ints: 73
ints: 74
type: INTS
}
attribute {
name: "nodes_treeids"
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
type: INTS
}
attribute {
name: "nodes_truenodeids"
ints: 1
ints: 2
ints: 3
ints: 4
ints: 0
ints: 6
ints: 7
ints: 8
ints: 0
ints: 0
ints: 0
ints: 12
ints: 13
ints: 0
ints: 0
ints: 16
ints: 0
ints: 0
ints: 19
ints: 0
ints: 21
ints: 22
ints: 23
ints: 0
ints: 0
ints: 26
ints: 0
ints: 0
ints: 29
ints: 30
ints: 0
ints: 0
ints: 33
ints: 0
ints: 0
ints: 36
ints: 0
ints: 0
ints: 39
ints: 40
ints: 41
ints: 0
ints: 43
ints: 0
ints: 0
ints: 46
ints: 47
ints: 48
ints: 49
ints: 0
ints: 0
ints: 0
ints: 53
ints: 54
ints: 0
ints: 0
ints: 0
ints: 58
ints: 59
ints: 0
ints: 61
ints: 0
ints: 0
ints: 64
ints: 65
ints: 0
ints: 0
ints: 68
ints: 0
ints: 0
ints: 71
ints: 72
ints: 0
ints: 0
ints: 0
type: INTS
}
attribute {
name: "nodes_values"
floats: 4.625307559967041
floats: 0.2565509080886841
floats: -4.543979644775391
floats: -5.656089782714844
floats: 0.0
floats: -9.38259220123291
floats: -9.46268081665039
floats: -4.977330684661865
floats: 0.0
floats: 0.0
floats: 0.0
floats: -8.918272972106934
floats: -4.67173433303833
floats: 0.0
floats: 0.0
floats: -7.388498783111572
floats: 0.0
floats: 0.0
floats: -11.878083229064941
floats: 0.0
floats: -8.927239418029785
floats: -3.3035221099853516
floats: -4.1548967361450195
floats: 0.0
floats: 0.0
floats: -9.733203887939453
floats: 0.0
floats: 0.0
floats: -8.641737937927246
floats: -8.793888092041016
floats: 0.0
floats: 0.0
floats: -8.127656936645508
floats: 0.0
floats: 0.0
floats: 4.463893890380859
floats: 0.0
floats: 0.0
floats: 9.995587348937988
floats: 5.7912702560424805
floats: 5.462047100067139
floats: 0.0
floats: 5.694032669067383
floats: 0.0
floats: 0.0
floats: -0.20998094975948334
floats: 8.954193115234375
floats: 8.604443550109863
floats: 8.177303314208984
floats: 0.0
floats: 0.0
floats: 0.0
floats: -0.8780495524406433
floats: -1.739040493965149
floats: 0.0
floats: 0.0
floats: 0.0
floats: 8.433889389038086
floats: 3.0417778491973877
floats: 0.0
floats: 7.852134704589844
floats: 0.0
floats: 0.0
floats: 9.927783012390137
floats: 9.33053970336914
floats: 0.0
floats: 0.0
floats: 10.304119110107422
floats: 0.0
floats: 0.0
floats: 10.273776054382324
floats: 10.0852689743042
floats: 0.0
floats: 0.0
floats: 0.0
type: FLOATS
}
attribute {
name: "post_transform"
s: "NONE"
type: STRING
}
attribute {
name: "target_ids"
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
type: INTS
}
attribute {
name: "target_nodeids"
ints: 4
ints: 8
ints: 9
ints: 10
ints: 13
ints: 14
ints: 16
ints: 17
ints: 19
ints: 23
ints: 24
ints: 26
ints: 27
ints: 30
ints: 31
ints: 33
ints: 34
ints: 36
ints: 37
ints: 41
ints: 43
ints: 44
ints: 49
ints: 50
ints: 51
ints: 54
ints: 55
ints: 56
ints: 59
ints: 61
ints: 62
ints: 65
ints: 66
ints: 68
ints: 69
ints: 72
ints: 73
ints: 74
type: INTS
}
attribute {
name: "target_treeids"
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
type: INTS
}
attribute {
name: "target_weights"
floats: 4.0
floats: 8.0
floats: 9.0
floats: 10.0
floats: 13.0
floats: 14.0
floats: 16.0
floats: 17.0
floats: 19.0
floats: 23.0
floats: 24.0
floats: 26.0
floats: 27.0
floats: 30.0
floats: 31.0
floats: 33.0
floats: 34.0
floats: 36.0
floats: 37.0
floats: 41.0
floats: 43.0
floats: 44.0
floats: 49.0
floats: 50.0
floats: 51.0
floats: 54.0
floats: 55.0
floats: 56.0
floats: 59.0
floats: 61.0
floats: 62.0
floats: 65.0
floats: 66.0
floats: 68.0
floats: 69.0
floats: 72.0
floats: 73.0
floats: 74.0
type: FLOATS
}
domain: "ai.onnx.ml"
}
node {
input: "X"
input: "node_sample0_Gathercst"
output: "node_sample1_output0"
name: "node_sample1_Gather"
op_type: "Gather"
attribute {
name: "axis"
i: 1
type: INT
}
domain: ""
}
node {
input: "X"
input: "node_sample0_Gathercst"
output: "node_sample2_output0"
name: "node_sample2_Gather"
op_type: "Gather"
attribute {
name: "axis"
i: 1
type: INT
}
domain: ""
}
node {
input: "node_sample0_Y0"
output: "node_sample0_output02"
name: "node_sample0_Cast"
op_type: "Cast"
attribute {
name: "to"
i: 7
type: INT
}
domain: ""
}
node {
input: "node_sample0_Y0"
output: "path_length0_output0"
name: "path_length0_Cast"
op_type: "Cast"
attribute {
name: "to"
i: 7
type: INT
}
domain: ""
}
node {
input: "node_sample1_output0"
output: "node_sample1_Y0"
name: "node_sample1_TreeEnsembleRegressor"
op_type: "TreeEnsembleRegressor"
attribute {
name: "n_targets"
i: 1
type: INT
}
attribute {
name: "nodes_falsenodeids"
ints: 16
ints: 5
ints: 4
ints: 0
ints: 0
ints: 13
ints: 8
ints: 0
ints: 12
ints: 11
ints: 0
ints: 0
ints: 0
ints: 15
ints: 0
ints: 0
ints: 62
ints: 37
ints: 24
ints: 23
ints: 22
ints: 0
ints: 0
ints: 0
ints: 32
ints: 29
ints: 28
ints: 0
ints: 0
ints: 31
ints: 0
ints: 0
ints: 34
ints: 0
ints: 36
ints: 0
ints: 0
ints: 51
ints: 46
ints: 43
ints: 42
ints: 0
ints: 0
ints: 45
ints: 0
ints: 0
ints: 48
ints: 0
ints: 50
ints: 0
ints: 0
ints: 57
ints: 56
ints: 55
ints: 0
ints: 0
ints: 0
ints: 59
ints: 0
ints: 61
ints: 0
ints: 0
ints: 76
ints: 75
ints: 70
ints: 67
ints: 0
ints: 69
ints: 0
ints: 0
ints: 72
ints: 0
ints: 74
ints: 0
ints: 0
ints: 0
ints: 86
ints: 81
ints: 80
ints: 0
ints: 0
ints: 85
ints: 84
ints: 0
ints: 0
ints: 0
ints: 0
type: INTS
}
attribute {
name: "nodes_featureids"
ints: 0
ints: 1
ints: 0
ints: 0
ints: 0
ints: 1
ints: 0
ints: 0
ints: 1
ints: 1
ints: 0
ints: 0
ints: 0
ints: 1
ints: 0
ints: 0
ints: 1
ints: 0
ints: 1
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 1
ints: 1
ints: 1
ints: 0
ints: 0
ints: 1
ints: 0
ints: 0
ints: 1
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 1
ints: 1
ints: 1
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 1
ints: 0
ints: 1
ints: 0
ints: 0
ints: 1
ints: 1
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 1
ints: 0
ints: 0
ints: 1
ints: 0
ints: 1
ints: 0
ints: 0
ints: 1
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 1
ints: 0
ints: 0
ints: 0
ints: 1
ints: 0
ints: 0
ints: 0
ints: 0
type: INTS
}
attribute {
name: "nodes_hitrates"
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
type: FLOATS
}
attribute {
name: "nodes_missing_value_tracks_true"
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
type: INTS
}
attribute {
name: "nodes_modes"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "LEAF"
strings: "LEAF"
type: STRINGS
}
attribute {
name: "nodes_nodeids"
ints: 0
ints: 1
ints: 2
ints: 3
ints: 4
ints: 5
ints: 6
ints: 7
ints: 8
ints: 9
ints: 10
ints: 11
ints: 12
ints: 13
ints: 14
ints: 15
ints: 16
ints: 17
ints: 18
ints: 19
ints: 20
ints: 21
ints: 22
ints: 23
ints: 24
ints: 25
ints: 26
ints: 27
ints: 28
ints: 29
ints: 30
ints: 31
ints: 32
ints: 33
ints: 34
ints: 35
ints: 36
ints: 37
ints: 38
ints: 39
ints: 40
ints: 41
ints: 42
ints: 43
ints: 44
ints: 45
ints: 46
ints: 47
ints: 48
ints: 49
ints: 50
ints: 51
ints: 52
ints: 53
ints: 54
ints: 55
ints: 56
ints: 57
ints: 58
ints: 59
ints: 60
ints: 61
ints: 62
ints: 63
ints: 64
ints: 65
ints: 66
ints: 67
ints: 68
ints: 69
ints: 70
ints: 71
ints: 72
ints: 73
ints: 74
ints: 75
ints: 76
ints: 77
ints: 78
ints: 79
ints: 80
ints: 81
ints: 82
ints: 83
ints: 84
ints: 85
ints: 86
type: INTS
}
attribute {
name: "nodes_treeids"
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
type: INTS
}
attribute {
name: "nodes_truenodeids"
ints: 1
ints: 2
ints: 3
ints: 0
ints: 0
ints: 6
ints: 7
ints: 0
ints: 9
ints: 10
ints: 0
ints: 0
ints: 0
ints: 14
ints: 0
ints: 0
ints: 17
ints: 18
ints: 19
ints: 20
ints: 21
ints: 0
ints: 0
ints: 0
ints: 25
ints: 26
ints: 27
ints: 0
ints: 0
ints: 30
ints: 0
ints: 0
ints: 33
ints: 0
ints: 35
ints: 0
ints: 0
ints: 38
ints: 39
ints: 40
ints: 41
ints: 0
ints: 0
ints: 44
ints: 0
ints: 0
ints: 47
ints: 0
ints: 49
ints: 0
ints: 0
ints: 52
ints: 53
ints: 54
ints: 0
ints: 0
ints: 0
ints: 58
ints: 0
ints: 60
ints: 0
ints: 0
ints: 63
ints: 64
ints: 65
ints: 66
ints: 0
ints: 68
ints: 0
ints: 0
ints: 71
ints: 0
ints: 73
ints: 0
ints: 0
ints: 0
ints: 77
ints: 78
ints: 79
ints: 0
ints: 0
ints: 82
ints: 83
ints: 0
ints: 0
ints: 0
ints: 0
type: INTS
}
attribute {
name: "nodes_values"
floats: -9.646719932556152
floats: -4.0288848876953125
floats: -10.029373168945312
floats: 0.0
floats: 0.0
floats: -3.3176441192626953
floats: -10.441263198852539
floats: 0.0
floats: -3.821807622909546
floats: -3.8326830863952637
floats: 0.0
floats: 0.0
floats: 0.0
floats: -3.0684330463409424
floats: 0.0
floats: 0.0
floats: 7.379856109619141
floats: -0.6280852556228638
floats: -5.455114841461182
floats: -8.343465805053711
floats: -8.74602222442627
floats: 0.0
floats: 0.0
floats: 0.0
floats: -4.3578948974609375
floats: -5.1702165603637695
floats: -5.311067581176758
floats: 0.0
floats: 0.0
floats: -4.462330341339111
floats: 0.0
floats: 0.0
floats: -4.17664909362793
floats: 0.0
floats: -8.186973571777344
floats: 0.0
floats: 0.0
floats: 8.978128433227539
floats: 3.6531522274017334
floats: -0.6954878568649292
floats: -1.8641395568847656
floats: 0.0
floats: 0.0
floats: 8.39435863494873
floats: 0.0
floats: 0.0
floats: 6.207352638244629
floats: 0.0
floats: 7.171633243560791
floats: 0.0
floats: 0.0
floats: -1.2392456531524658
floats: -1.3909186124801636
floats: 9.704169273376465
floats: 0.0
floats: 0.0
floats: 0.0
floats: 9.485459327697754
floats: 0.0
floats: -0.8870576620101929
floats: 0.0
floats: 0.0
floats: 9.624462127685547
floats: 8.097334861755371
floats: 7.6462554931640625
floats: 5.629445552825928
floats: 0.0
floats: 7.46665620803833
floats: 0.0
floats: 0.0
floats: 4.771121025085449
floats: 0.0
floats: 6.590895652770996
floats: 0.0
floats: 0.0
floats: 0.0
floats: 7.104705333709717
floats: 6.5459465980529785
floats: 10.071699142456055
floats: 0.0
floats: 0.0
floats: 6.608224868774414
floats: 10.278341293334961
floats: 0.0
floats: 0.0
floats: 0.0
floats: 0.0
type: FLOATS
}
attribute {
name: "post_transform"
s: "NONE"
type: STRING
}
attribute {
name: "target_ids"
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
type: INTS
}
attribute {
name: "target_nodeids"
ints: 3
ints: 4
ints: 7
ints: 10
ints: 11
ints: 12
ints: 14
ints: 15
ints: 21
ints: 22
ints: 23
ints: 27
ints: 28
ints: 30
ints: 31
ints: 33
ints: 35
ints: 36
ints: 41
ints: 42
ints: 44
ints: 45
ints: 47
ints: 49
ints: 50
ints: 54
ints: 55
ints: 56
ints: 58
ints: 60
ints: 61
ints: 66
ints: 68
ints: 69
ints: 71
ints: 73
ints: 74
ints: 75
ints: 79
ints: 80
ints: 83
ints: 84
ints: 85
ints: 86
type: INTS
}
attribute {
name: "target_treeids"
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
type: INTS
}
attribute {
name: "target_weights"
floats: 3.0
floats: 4.0
floats: 7.0
floats: 10.0
floats: 11.0
floats: 12.0
floats: 14.0
floats: 15.0
floats: 21.0
floats: 22.0
floats: 23.0
floats: 27.0
floats: 28.0
floats: 30.0
floats: 31.0
floats: 33.0
floats: 35.0
floats: 36.0
floats: 41.0
floats: 42.0
floats: 44.0
floats: 45.0
floats: 47.0
floats: 49.0
floats: 50.0
floats: 54.0
floats: 55.0
floats: 56.0
floats: 58.0
floats: 60.0
floats: 61.0
floats: 66.0
floats: 68.0
floats: 69.0
floats: 71.0
floats: 73.0
floats: 74.0
floats: 75.0
floats: 79.0
floats: 80.0
floats: 83.0
floats: 84.0
floats: 85.0
floats: 86.0
type: FLOATS
}
domain: "ai.onnx.ml"
}
node {
input: "node_sample2_output0"
output: "node_sample2_Y0"
name: "node_sample2_TreeEnsembleRegressor"
op_type: "TreeEnsembleRegressor"
attribute {
name: "n_targets"
i: 1
type: INT
}
attribute {
name: "nodes_falsenodeids"
ints: 32
ints: 29
ints: 28
ints: 13
ints: 6
ints: 0
ints: 10
ints: 9
ints: 0
ints: 0
ints: 12
ints: 0
ints: 0
ints: 21
ints: 18
ints: 17
ints: 0
ints: 0
ints: 20
ints: 0
ints: 0
ints: 25
ints: 24
ints: 0
ints: 0
ints: 27
ints: 0
ints: 0
ints: 0
ints: 31
ints: 0
ints: 0
ints: 80
ints: 55
ints: 46
ints: 39
ints: 38
ints: 0
ints: 0
ints: 43
ints: 42
ints: 0
ints: 0
ints: 45
ints: 0
ints: 0
ints: 48
ints: 0
ints: 52
ints: 51
ints: 0
ints: 0
ints: 54
ints: 0
ints: 0
ints: 69
ints: 62
ints: 59
ints: 0
ints: 61
ints: 0
ints: 0
ints: 66
ints: 65
ints: 0
ints: 0
ints: 68
ints: 0
ints: 0
ints: 73
ints: 72
ints: 0
ints: 0
ints: 77
ints: 76
ints: 0
ints: 0
ints: 79
ints: 0
ints: 0
ints: 90
ints: 89
ints: 86
ints: 85
ints: 0
ints: 0
ints: 88
ints: 0
ints: 0
ints: 0
ints: 0
type: INTS
}
attribute {
name: "nodes_featureids"
ints: 0
ints: 0
ints: 1
ints: 1
ints: 0
ints: 0
ints: 0
ints: 1
ints: 0
ints: 0
ints: 1
ints: 0
ints: 0
ints: 0
ints: 1
ints: 1
ints: 0
ints: 0
ints: 1
ints: 0
ints: 0
ints: 1
ints: 0
ints: 0
ints: 0
ints: 1
ints: 0
ints: 0
ints: 0
ints: 1
ints: 0
ints: 0
ints: 1
ints: 1
ints: 1
ints: 1
ints: 1
ints: 0
ints: 0
ints: 1
ints: 0
ints: 0
ints: 0
ints: 1
ints: 0
ints: 0
ints: 1
ints: 0
ints: 1
ints: 0
ints: 0
ints: 0
ints: 1
ints: 0
ints: 0
ints: 1
ints: 0
ints: 1
ints: 0
ints: 0
ints: 0
ints: 0
ints: 1
ints: 1
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 1
ints: 0
ints: 0
ints: 0
ints: 1
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 1
ints: 0
ints: 0
ints: 0
ints: 0
ints: 1
ints: 0
ints: 0
ints: 0
ints: 0
type: INTS
}
attribute {
name: "nodes_hitrates"
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
type: FLOATS
}
attribute {
name: "nodes_missing_value_tracks_true"
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
type: INTS
}
attribute {
name: "nodes_modes"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "BRANCH_LEQ"
strings: "LEAF"
strings: "LEAF"
strings: "LEAF"
strings: "LEAF"
type: STRINGS
}
attribute {
name: "nodes_nodeids"
ints: 0
ints: 1
ints: 2
ints: 3
ints: 4
ints: 5
ints: 6
ints: 7
ints: 8
ints: 9
ints: 10
ints: 11
ints: 12
ints: 13
ints: 14
ints: 15
ints: 16
ints: 17
ints: 18
ints: 19
ints: 20
ints: 21
ints: 22
ints: 23
ints: 24
ints: 25
ints: 26
ints: 27
ints: 28
ints: 29
ints: 30
ints: 31
ints: 32
ints: 33
ints: 34
ints: 35
ints: 36
ints: 37
ints: 38
ints: 39
ints: 40
ints: 41
ints: 42
ints: 43
ints: 44
ints: 45
ints: 46
ints: 47
ints: 48
ints: 49
ints: 50
ints: 51
ints: 52
ints: 53
ints: 54
ints: 55
ints: 56
ints: 57
ints: 58
ints: 59
ints: 60
ints: 61
ints: 62
ints: 63
ints: 64
ints: 65
ints: 66
ints: 67
ints: 68
ints: 69
ints: 70
ints: 71
ints: 72
ints: 73
ints: 74
ints: 75
ints: 76
ints: 77
ints: 78
ints: 79
ints: 80
ints: 81
ints: 82
ints: 83
ints: 84
ints: 85
ints: 86
ints: 87
ints: 88
ints: 89
ints: 90
type: INTS
}
attribute {
name: "nodes_treeids"
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
type: INTS
}
attribute {
name: "nodes_truenodeids"
ints: 1
ints: 2
ints: 3
ints: 4
ints: 5
ints: 0
ints: 7
ints: 8
ints: 0
ints: 0
ints: 11
ints: 0
ints: 0
ints: 14
ints: 15
ints: 16
ints: 0
ints: 0
ints: 19
ints: 0
ints: 0
ints: 22
ints: 23
ints: 0
ints: 0
ints: 26
ints: 0
ints: 0
ints: 0
ints: 30
ints: 0
ints: 0
ints: 33
ints: 34
ints: 35
ints: 36
ints: 37
ints: 0
ints: 0
ints: 40
ints: 41
ints: 0
ints: 0
ints: 44
ints: 0
ints: 0
ints: 47
ints: 0
ints: 49
ints: 50
ints: 0
ints: 0
ints: 53
ints: 0
ints: 0
ints: 56
ints: 57
ints: 58
ints: 0
ints: 60
ints: 0
ints: 0
ints: 63
ints: 64
ints: 0
ints: 0
ints: 67
ints: 0
ints: 0
ints: 70
ints: 71
ints: 0
ints: 0
ints: 74
ints: 75
ints: 0
ints: 0
ints: 78
ints: 0
ints: 0
ints: 81
ints: 82
ints: 83
ints: 84
ints: 0
ints: 0
ints: 87
ints: 0
ints: 0
ints: 0
ints: 0
type: INTS
}
attribute {
name: "nodes_values"
floats: 4.753084182739258
floats: -4.926673889160156
floats: -2.1489949226379395
floats: -3.283708333969116
floats: -10.509230613708496
floats: 0.0
floats: -8.01207447052002
floats: -4.874238014221191
floats: 0.0
floats: 0.0
floats: -3.9940476417541504
floats: 0.0
floats: 0.0
floats: -9.16932201385498
floats: -2.9985861778259277
floats: -3.186936855316162
floats: 0.0
floats: 0.0
floats: -2.893580913543701
floats: 0.0
floats: 0.0
floats: -2.9329171180725098
floats: -8.762840270996094
floats: 0.0
floats: 0.0
floats: -2.765859365463257
floats: 0.0
floats: 0.0
floats: 0.0
floats: 8.105192184448242
floats: 0.0
floats: 0.0
floats: 9.585651397705078
floats: 3.496211051940918
floats: -2.1036152839660645
floats: -2.6763384342193604
floats: -2.8757870197296143
floats: 0.0
floats: 0.0
floats: -2.265378475189209
floats: 8.213210105895996
floats: 0.0
floats: 0.0
floats: -2.2207179069519043
floats: 0.0
floats: 0.0
floats: -1.859968900680542
floats: 0.0
floats: -0.5661556720733643
floats: 6.7569379806518555
floats: 0.0
floats: 0.0
floats: -0.2569067180156708
floats: 0.0
floats: 0.0
floats: 8.686277389526367
floats: 6.566116809844971
floats: 6.910106182098389
floats: 0.0
floats: 5.274450302124023
floats: 0.0
floats: 0.0
floats: 7.502935409545898
floats: 6.110256195068359
floats: 0.0
floats: 0.0
floats: 7.0757975578308105
floats: 0.0
floats: 0.0
floats: 6.322373867034912
floats: 9.130280494689941
floats: 0.0
floats: 0.0
floats: 6.885787010192871
floats: 8.82026195526123
floats: 0.0
floats: 0.0
floats: 7.328880310058594
floats: 0.0
floats: 0.0
floats: 7.135841369628906
floats: 10.441483497619629
floats: 6.537355422973633
floats: 6.391022205352783
floats: 0.0
floats: 0.0
floats: 9.915621757507324
floats: 0.0
floats: 0.0
floats: 0.0
floats: 0.0
type: FLOATS
}
attribute {
name: "post_transform"
s: "NONE"
type: STRING
}
attribute {
name: "target_ids"
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
type: INTS
}
attribute {
name: "target_nodeids"
ints: 5
ints: 8
ints: 9
ints: 11
ints: 12
ints: 16
ints: 17
ints: 19
ints: 20
ints: 23
ints: 24
ints: 26
ints: 27
ints: 28
ints: 30
ints: 31
ints: 37
ints: 38
ints: 41
ints: 42
ints: 44
ints: 45
ints: 47
ints: 50
ints: 51
ints: 53
ints: 54
ints: 58
ints: 60
ints: 61
ints: 64
ints: 65
ints: 67
ints: 68
ints: 71
ints: 72
ints: 75
ints: 76
ints: 78
ints: 79
ints: 84
ints: 85
ints: 87
ints: 88
ints: 89
ints: 90
type: INTS
}
attribute {
name: "target_treeids"
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
ints: 0
type: INTS
}
attribute {
name: "target_weights"
floats: 5.0
floats: 8.0
floats: 9.0
floats: 11.0
floats: 12.0
floats: 16.0
floats: 17.0
floats: 19.0
floats: 20.0
floats: 23.0
floats: 24.0
floats: 26.0
floats: 27.0
floats: 28.0
floats: 30.0
floats: 31.0
floats: 37.0
floats: 38.0
floats: 41.0
floats: 42.0
floats: 44.0
floats: 45.0
floats: 47.0
floats: 50.0
floats: 51.0
floats: 53.0
floats: 54.0
floats: 58.0
floats: 60.0
floats: 61.0
floats: 64.0
floats: 65.0
floats: 67.0
floats: 68.0
floats: 71.0
floats: 72.0
floats: 75.0
floats: 76.0
floats: 78.0
floats: 79.0
floats: 84.0
floats: 85.0
floats: 87.0
floats: 88.0
floats: 89.0
floats: 90.0
type: FLOATS
}
domain: "ai.onnx.ml"
}
node {
input: "node_sample0_output02"
output: "node_sample0_Y02"
name: "node_sample0_LabelEncoder"
op_type: "LabelEncoder"
attribute {
name: "keys_int64s"
ints: 4
ints: 8
ints: 9
ints: 10
ints: 13
ints: 14
ints: 16
ints: 17
ints: 19
ints: 23
ints: 24
ints: 26
ints: 27
ints: 30
ints: 31
ints: 33
ints: 34
ints: 36
ints: 37
ints: 41
ints: 43
ints: 44
ints: 49
ints: 50
ints: 51
ints: 54
ints: 55
ints: 56
ints: 59
ints: 61
ints: 62
ints: 65
ints: 66
ints: 68
ints: 69
ints: 72
ints: 73
ints: 74
type: INTS
}
attribute {
name: "values_floats"
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 4.0
floats: 1.0
floats: 1.0
floats: 4.0
floats: 6.0
floats: 1.0
floats: 2.0
floats: 2.0
floats: 1.0
floats: 4.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 16.0
floats: 5.0
floats: 1.0
floats: 4.0
floats: 2.0
floats: 1.0
floats: 1.0
floats: 13.0
floats: 1.0
floats: 7.0
floats: 3.0
floats: 3.0
floats: 2.0
floats: 1.0
floats: 1.0
floats: 1.0
type: FLOATS
}
domain: "ai.onnx.ml"
}
node {
input: "path_length0_output0"
output: "path_length0_Y0"
name: "path_length0_LabelEncoder"
op_type: "LabelEncoder"
attribute {
name: "keys_int64s"
ints: 4
ints: 8
ints: 9
ints: 10
ints: 13
ints: 14
ints: 16
ints: 17
ints: 19
ints: 23
ints: 24
ints: 26
ints: 27
ints: 30
ints: 31
ints: 33
ints: 34
ints: 36
ints: 37
ints: 41
ints: 43
ints: 44
ints: 49
ints: 50
ints: 51
ints: 54
ints: 55
ints: 56
ints: 59
ints: 61
ints: 62
ints: 65
ints: 66
ints: 68
ints: 69
ints: 72
ints: 73
ints: 74
type: INTS
}
attribute {
name: "values_floats"
floats: 5.0
floats: 8.0
floats: 8.0
floats: 7.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 5.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 4.0
floats: 4.0
floats: 5.0
floats: 6.0
floats: 6.0
floats: 8.0
floats: 8.0
floats: 7.0
floats: 8.0
floats: 8.0
floats: 7.0
floats: 7.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 5.0
floats: 5.0
floats: 4.0
type: FLOATS
}
domain: "ai.onnx.ml"
}
node {
input: "node_sample2_Y0"
output: "node_sample2_output02"
name: "node_sample2_Cast"
op_type: "Cast"
attribute {
name: "to"
i: 7
type: INT
}
domain: ""
}
node {
input: "node_sample2_Y0"
output: "path_length2_output0"
name: "path_length2_Cast"
op_type: "Cast"
attribute {
name: "to"
i: 7
type: INT
}
domain: ""
}
node {
input: "node_sample1_Y0"
output: "path_length1_output0"
name: "path_length1_Cast"
op_type: "Cast"
attribute {
name: "to"
i: 7
type: INT
}
domain: ""
}
node {
input: "node_sample1_Y0"
output: "node_sample1_output02"
name: "node_sample1_Cast"
op_type: "Cast"
attribute {
name: "to"
i: 7
type: INT
}
domain: ""
}
node {
input: "node_sample0_Y02"
input: "path_length0_Reshapecst"
output: "node_sample0_reshaped0"
name: "node_sample0_Reshape"
op_type: "Reshape"
attribute {
name: "allowzero"
i: 0
type: INT
}
domain: ""
}
node {
input: "path_length0_Y0"
input: "path_length0_Reshapecst"
output: "path_length0_reshaped0"
name: "path_length0_Reshape"
op_type: "Reshape"
attribute {
name: "allowzero"
i: 0
type: INT
}
domain: ""
}
node {
input: "path_length2_output0"
output: "path_length2_Y0"
name: "path_length2_LabelEncoder"
op_type: "LabelEncoder"
attribute {
name: "keys_int64s"
ints: 5
ints: 8
ints: 9
ints: 11
ints: 12
ints: 16
ints: 17
ints: 19
ints: 20
ints: 23
ints: 24
ints: 26
ints: 27
ints: 28
ints: 30
ints: 31
ints: 37
ints: 38
ints: 41
ints: 42
ints: 44
ints: 45
ints: 47
ints: 50
ints: 51
ints: 53
ints: 54
ints: 58
ints: 60
ints: 61
ints: 64
ints: 65
ints: 67
ints: 68
ints: 71
ints: 72
ints: 75
ints: 76
ints: 78
ints: 79
ints: 84
ints: 85
ints: 87
ints: 88
ints: 89
ints: 90
type: INTS
}
attribute {
name: "values_floats"
floats: 6.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 4.0
floats: 4.0
floats: 4.0
floats: 7.0
floats: 7.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 6.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 7.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 7.0
floats: 7.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 7.0
floats: 7.0
floats: 7.0
floats: 7.0
floats: 5.0
floats: 4.0
type: FLOATS
}
domain: "ai.onnx.ml"
}
node {
input: "node_sample2_output02"
output: "node_sample2_Y02"
name: "node_sample2_LabelEncoder"
op_type: "LabelEncoder"
attribute {
name: "keys_int64s"
ints: 5
ints: 8
ints: 9
ints: 11
ints: 12
ints: 16
ints: 17
ints: 19
ints: 20
ints: 23
ints: 24
ints: 26
ints: 27
ints: 28
ints: 30
ints: 31
ints: 37
ints: 38
ints: 41
ints: 42
ints: 44
ints: 45
ints: 47
ints: 50
ints: 51
ints: 53
ints: 54
ints: 58
ints: 60
ints: 61
ints: 64
ints: 65
ints: 67
ints: 68
ints: 71
ints: 72
ints: 75
ints: 76
ints: 78
ints: 79
ints: 84
ints: 85
ints: 87
ints: 88
ints: 89
ints: 90
type: INTS
}
attribute {
name: "values_floats"
floats: 1.0
floats: 5.0
floats: 13.0
floats: 3.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 2.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 3.0
floats: 4.0
floats: 2.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 14.0
floats: 3.0
floats: 2.0
floats: 1.0
floats: 1.0
floats: 5.0
floats: 1.0
floats: 3.0
floats: 1.0
floats: 6.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 3.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
type: FLOATS
}
domain: "ai.onnx.ml"
}
node {
input: "path_length1_output0"
output: "path_length1_Y0"
name: "path_length1_LabelEncoder"
op_type: "LabelEncoder"
attribute {
name: "keys_int64s"
ints: 3
ints: 4
ints: 7
ints: 10
ints: 11
ints: 12
ints: 14
ints: 15
ints: 21
ints: 22
ints: 23
ints: 27
ints: 28
ints: 30
ints: 31
ints: 33
ints: 35
ints: 36
ints: 41
ints: 42
ints: 44
ints: 45
ints: 47
ints: 49
ints: 50
ints: 54
ints: 55
ints: 56
ints: 58
ints: 60
ints: 61
ints: 66
ints: 68
ints: 69
ints: 71
ints: 73
ints: 74
ints: 75
ints: 79
ints: 80
ints: 83
ints: 84
ints: 85
ints: 86
type: INTS
}
attribute {
name: "values_floats"
floats: 4.0
floats: 4.0
floats: 5.0
floats: 7.0
floats: 7.0
floats: 6.0
floats: 5.0
floats: 5.0
floats: 7.0
floats: 7.0
floats: 6.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 7.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 7.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 8.0
floats: 7.0
floats: 7.0
floats: 8.0
floats: 8.0
floats: 7.0
floats: 8.0
floats: 8.0
floats: 7.0
floats: 8.0
floats: 8.0
floats: 5.0
floats: 7.0
floats: 7.0
floats: 8.0
floats: 8.0
floats: 7.0
floats: 5.0
type: FLOATS
}
domain: "ai.onnx.ml"
}
node {
input: "node_sample1_output02"
output: "node_sample1_Y02"
name: "node_sample1_LabelEncoder"
op_type: "LabelEncoder"
attribute {
name: "keys_int64s"
ints: 3
ints: 4
ints: 7
ints: 10
ints: 11
ints: 12
ints: 14
ints: 15
ints: 21
ints: 22
ints: 23
ints: 27
ints: 28
ints: 30
ints: 31
ints: 33
ints: 35
ints: 36
ints: 41
ints: 42
ints: 44
ints: 45
ints: 47
ints: 49
ints: 50
ints: 54
ints: 55
ints: 56
ints: 58
ints: 60
ints: 61
ints: 66
ints: 68
ints: 69
ints: 71
ints: 73
ints: 74
ints: 75
ints: 79
ints: 80
ints: 83
ints: 84
ints: 85
ints: 86
type: INTS
}
attribute {
name: "values_floats"
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 6.0
floats: 1.0
floats: 1.0
floats: 11.0
floats: 1.0
floats: 8.0
floats: 12.0
floats: 1.0
floats: 2.0
floats: 1.0
floats: 2.0
floats: 1.0
floats: 1.0
floats: 5.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 3.0
floats: 1.0
floats: 1.0
floats: 7.0
floats: 10.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
floats: 1.0
type: FLOATS
}
domain: "ai.onnx.ml"
}
node {
input: "node_sample0_reshaped0"
input: "dec_Powcst"
output: "eq2_0_C0"
name: "eq2_0_Equal"
op_type: "Equal"
domain: ""
}
node {
input: "node_sample0_reshaped0"
input: "dec_Powcst"
output: "plus2_0_C0"
name: "plus2_0_Greater"
op_type: "Greater"
domain: ""
}
node {
input: "node_sample2_Y02"
input: "path_length0_Reshapecst"
output: "node_sample2_reshaped0"
name: "node_sample2_Reshape"
op_type: "Reshape"
attribute {
name: "allowzero"
i: 0
type: INT
}
domain: ""
}
node {
input: "path_length2_Y0"
input: "path_length0_Reshapecst"
output: "path_length2_reshaped0"
name: "path_length2_Reshape"
op_type: "Reshape"
attribute {
name: "allowzero"
i: 0
type: INT
}
domain: ""
}
node {
input: "path_length1_Y0"
input: "path_length0_Reshapecst"
output: "path_length1_reshaped0"
name: "path_length1_Reshape"
op_type: "Reshape"
attribute {
name: "allowzero"
i: 0
type: INT
}
domain: ""
}
node {
input: "node_sample1_Y02"
input: "path_length0_Reshapecst"
output: "node_sample1_reshaped0"
name: "node_sample1_Reshape"
op_type: "Reshape"
attribute {
name: "allowzero"
i: 0
type: INT
}
domain: ""
}
node {
input: "plus2_0_C0"
output: "plus2_0_output0"
name: "plus2_0_Cast"
op_type: "Cast"
attribute {
name: "to"
i: 1
type: INT
}
domain: ""
}
node {
input: "eq2_0_C0"
output: "eq2_0_output0"
name: "eq2_0_Cast"
op_type: "Cast"
attribute {
name: "to"
i: 1
type: INT
}
domain: ""
}
node {
input: "node_sample2_reshaped0"
input: "dec_Powcst"
output: "eq2_2_C0"
name: "eq2_2_Equal"
op_type: "Equal"
domain: ""
}
node {
input: "node_sample2_reshaped0"
input: "dec_Powcst"
output: "plus2_2_C0"
name: "plus2_2_Greater"
op_type: "Greater"
domain: ""
}
node {
input: "node_sample1_reshaped0"
input: "dec_Powcst"
output: "plus2_1_C0"
name: "plus2_1_Greater"
op_type: "Greater"
domain: ""
}
node {
input: "node_sample1_reshaped0"
input: "dec_Powcst"
output: "eq2_1_C0"
name: "eq2_1_Equal"
op_type: "Equal"
domain: ""
}
node {
input: "plus2_0_output0"
input: "node_sample0_reshaped0"
output: "eqp2ps0_C0"
name: "eqp2ps0_Mul"
op_type: "Mul"
domain: ""
}
node {
input: "eq2_2_C0"
output: "eq2_2_output0"
name: "eq2_2_Cast"
op_type: "Cast"
attribute {
name: "to"
i: 1
type: INT
}
domain: ""
}
node {
input: "plus2_2_C0"
output: "plus2_2_output0"
name: "plus2_2_Cast"
op_type: "Cast"
attribute {
name: "to"
i: 1
type: INT
}
domain: ""
}
node {
input: "eq2_1_C0"
output: "eq2_1_output0"
name: "eq2_1_Cast"
op_type: "Cast"
attribute {
name: "to"
i: 1
type: INT
}
domain: ""
}
node {
input: "plus2_1_C0"
output: "plus2_1_output0"
name: "plus2_1_Cast"
op_type: "Cast"
attribute {
name: "to"
i: 1
type: INT
}
domain: ""
}
node {
input: "eqp2ps0_C0"
input: "eqp2p_m1_0_Addcst"
output: "eqp2p_m1_0_C0"
name: "eqp2p_m1_0_Add"
op_type: "Add"
domain: ""
}
node {
input: "eqp2ps0_C0"
input: "eqp2p_m1_0_Maxcst"
output: "eqp_ns0_max0"
name: "eqp_ns0_Max"
op_type: "Max"
domain: ""
}
node {
input: "plus2_2_output0"
input: "node_sample2_reshaped0"
output: "eqp2ps2_C0"
name: "eqp2ps2_Mul"
op_type: "Mul"
domain: ""
}
node {
input: "plus2_1_output0"
input: "node_sample1_reshaped0"
output: "eqp2ps1_C0"
name: "eqp2ps1_Mul"
op_type: "Mul"
domain: ""
}
node {
input: "eqp2p_m1_0_C0"
input: "eqp2p_m1_0_Maxcst1"
output: "eqp2p_m1_0_max02"
name: "eqp2p_m1_0_Max1"
op_type: "Max"
domain: ""
}
node {
input: "eqp2p_m1_0_C0"
input: "eqp2p_m1_0_Maxcst"
output: "eqp2p_m1_0_max0"
name: "eqp2p_m1_0_Max"
op_type: "Max"
domain: ""
}
node {
input: "eqp2ps2_C0"
input: "eqp2p_m1_0_Maxcst"
output: "eqp_ns2_max0"
name: "eqp_ns2_Max"
op_type: "Max"
domain: ""
}
node {
input: "eqp2ps2_C0"
input: "eqp2p_m1_0_Addcst"
output: "eqp2p_m1_2_C0"
name: "eqp2p_m1_2_Add"
op_type: "Add"
domain: ""
}
node {
input: "eqp2ps1_C0"
input: "eqp2p_m1_0_Addcst"
output: "eqp2p_m1_1_C0"
name: "eqp2p_m1_1_Add"
op_type: "Add"
domain: ""
}
node {
input: "eqp2ps1_C0"
input: "eqp2p_m1_0_Maxcst"
output: "eqp_ns1_max0"
name: "eqp_ns1_Max"
op_type: "Max"
domain: ""
}
node {
input: "eqp2p_m1_0_max0"
output: "eqp_log0_output0"
name: "eqp_log0_Log"
op_type: "Log"
domain: ""
}
node {
input: "eqp2p_m1_0_max02"
input: "eqp_ns0_max0"
output: "eqp_ns0_C01"
name: "eqp_ns0_Div"
op_type: "Div"
domain: ""
}
node {
input: "eqp2p_m1_2_C0"
input: "eqp2p_m1_0_Maxcst1"
output: "eqp2p_m1_2_max02"
name: "eqp2p_m1_2_Max1"
op_type: "Max"
domain: ""
}
node {
input: "eqp2p_m1_2_C0"
input: "eqp2p_m1_0_Maxcst"
output: "eqp2p_m1_2_max0"
name: "eqp2p_m1_2_Max"
op_type: "Max"
domain: ""
}
node {
input: "eqp2p_m1_1_C0"
input: "eqp2p_m1_0_Maxcst1"
output: "eqp2p_m1_1_max02"
name: "eqp2p_m1_1_Max1"
op_type: "Max"
domain: ""
}
node {
input: "eqp2p_m1_1_C0"
input: "eqp2p_m1_0_Maxcst"
output: "eqp2p_m1_1_max0"
name: "eqp2p_m1_1_Max"
op_type: "Max"
domain: ""
}
node {
input: "eqp_log0_output0"
input: "eqp_log0_Addcst"
output: "eqp_log0_C01"
name: "eqp_log0_Add"
op_type: "Add"
domain: ""
}
node {
input: "eqp_ns0_C01"
input: "eqp_ns0_Mulcst"
output: "eqp_ns0_C0"
name: "eqp_ns0_Mul"
op_type: "Mul"
domain: ""
}
node {
input: "eqp2p_m1_2_max02"
input: "eqp_ns2_max0"
output: "eqp_ns2_C01"
name: "eqp_ns2_Div"
op_type: "Div"
domain: ""
}
node {
input: "eqp2p_m1_2_max0"
output: "eqp_log2_output0"
name: "eqp_log2_Log"
op_type: "Log"
domain: ""
}
node {
input: "eqp2p_m1_1_max02"
input: "eqp_ns1_max0"
output: "eqp_ns1_C01"
name: "eqp_ns1_Div"
op_type: "Div"
domain: ""
}
node {
input: "eqp2p_m1_1_max0"
output: "eqp_log1_output0"
name: "eqp_log1_Log"
op_type: "Log"
domain: ""
}
node {
input: "eqp_log0_C01"
input: "dec_Powcst"
output: "eqp_log0_C0"
name: "eqp_log0_Mul"
op_type: "Mul"
domain: ""
}
node {
input: "eqp_log2_output0"
input: "eqp_log0_Addcst"
output: "eqp_log2_C01"
name: "eqp_log2_Add"
op_type: "Add"
domain: ""
}
node {
input: "eqp_ns2_C01"
input: "eqp_ns0_Mulcst"
output: "eqp_ns2_C0"
name: "eqp_ns2_Mul"
op_type: "Mul"
domain: ""
}
node {
input: "eqp_ns1_C01"
input: "eqp_ns0_Mulcst"
output: "eqp_ns1_C0"
name: "eqp_ns1_Mul"
op_type: "Mul"
domain: ""
}
node {
input: "eqp_log1_output0"
input: "eqp_log0_Addcst"
output: "eqp_log1_C01"
name: "eqp_log1_Add"
op_type: "Add"
domain: ""
}
node {
input: "eqp_log0_C0"
input: "eqp_ns0_C0"
output: "avlog0_C01"
name: "avlog0_Add"
op_type: "Add"
domain: ""
}
node {
input: "eqp_log2_C01"
input: "dec_Powcst"
output: "eqp_log2_C0"
name: "eqp_log2_Mul"
op_type: "Mul"
domain: ""
}
node {
input: "eqp_log1_C01"
input: "dec_Powcst"
output: "eqp_log1_C0"
name: "eqp_log1_Mul"
op_type: "Mul"
domain: ""
}
node {
input: "avlog0_C01"
input: "plus2_0_output0"
output: "avlog0_C0"
name: "avlog0_Mul"
op_type: "Mul"
domain: ""
}
node {
input: "eqp_log2_C0"
input: "eqp_ns2_C0"
output: "avlog2_C01"
name: "avlog2_Add"
op_type: "Add"
domain: ""
}
node {
input: "eqp_log1_C0"
input: "eqp_ns1_C0"
output: "avlog1_C01"
name: "avlog1_Add"
op_type: "Add"
domain: ""
}
node {
input: "eq2_0_output0"
input: "avlog0_C0"
output: "avpl0_C0"
name: "avpl0_Add"
op_type: "Add"
domain: ""
}
node {
input: "avlog2_C01"
input: "plus2_2_output0"
output: "avlog2_C0"
name: "avlog2_Mul"
op_type: "Mul"
domain: ""
}
node {
input: "avlog1_C01"
input: "plus2_1_output0"
output: "avlog1_C0"
name: "avlog1_Mul"
op_type: "Mul"
domain: ""
}
node {
input: "path_length0_reshaped0"
input: "avpl0_C0"
output: "depth0_C01"
name: "depth0_Add"
op_type: "Add"
domain: ""
}
node {
input: "eq2_2_output0"
input: "avlog2_C0"
output: "avpl2_C0"
name: "avpl2_Add"
op_type: "Add"
domain: ""
}
node {
input: "eq2_1_output0"
input: "avlog1_C0"
output: "avpl1_C0"
name: "avpl1_Add"
op_type: "Add"
domain: ""
}
node {
input: "depth0_C01"
input: "eqp2p_m1_0_Addcst"
output: "depth0_C0"
name: "depth0_Add1"
op_type: "Add"
domain: ""
}
node {
input: "path_length2_reshaped0"
input: "avpl2_C0"
output: "depth2_C01"
name: "depth2_Add"
op_type: "Add"
domain: ""
}
node {
input: "path_length1_reshaped0"
input: "avpl1_C0"
output: "depth1_C01"
name: "depth1_Add"
op_type: "Add"
domain: ""
}
node {
input: "depth1_C01"
input: "eqp2p_m1_0_Addcst"
output: "depth1_C0"
name: "depth1_Add1"
op_type: "Add"
domain: ""
}
node {
input: "depth2_C01"
input: "eqp2p_m1_0_Addcst"
output: "depth2_C0"
name: "depth2_Add1"
op_type: "Add"
domain: ""
}
node {
input: "depth0_C0"
input: "depth1_C0"
input: "depth2_C0"
output: "dec_sum0"
name: "dec_Sum"
op_type: "Sum"
domain: ""
}
node {
input: "dec_sum0"
input: "dec_Divcst"
output: "dec_C0"
name: "dec_Div"
op_type: "Div"
domain: ""
}
node {
input: "dec_C0"
output: "dec_Y01"
name: "dec_Neg"
op_type: "Neg"
domain: ""
}
node {
input: "dec_Powcst"
input: "dec_Y01"
output: "dec_Z0"
name: "dec_Pow"
op_type: "Pow"
domain: ""
}
node {
input: "dec_Z0"
output: "dec_Y0"
name: "dec_Neg1"
op_type: "Neg"
domain: ""
}
node {
input: "dec_Y0"
input: "dec_Addcst"
output: "scores"
name: "dec_Add"
op_type: "Add"
domain: ""
}
node {
input: "scores"
input: "eqp2p_m1_0_Maxcst1"
output: "predict_C01"
name: "predict_Less"
op_type: "Less"
domain: ""
}
node {
input: "predict_C01"
output: "predict_output0"
name: "predict_Cast"
op_type: "Cast"
attribute {
name: "to"
i: 7
type: INT
}
domain: ""
}
node {
input: "predict_output0"
input: "predict_Mulcst"
output: "predict_C0"
name: "predict_Mul"
op_type: "Mul"
domain: ""
}
node {
input: "predict_C0"
input: "predict_Addcst"
output: "label"
name: "predict_Add"
op_type: "Add"
domain: ""
}
name: "ONNX(IsolationForest)"
initializer {
dims: 1
data_type: 1
float_data: 2.0
name: "dec_Powcst"
}
initializer {
dims: 2
data_type: 7
int64_data: 0
int64_data: 1
name: "node_sample0_Gathercst"
}
initializer {
dims: 2
data_type: 7
int64_data: -1
int64_data: 1
name: "path_length0_Reshapecst"
}
initializer {
dims: 1
data_type: 1
float_data: -1.0
name: "eqp2p_m1_0_Addcst"
}
initializer {
dims: 1
data_type: 1
float_data: 1.0
name: "eqp2p_m1_0_Maxcst"
}
initializer {
dims: 1
data_type: 1
float_data: 0.5772156715393066
name: "eqp_log0_Addcst"
}
initializer {
dims: 1
data_type: 1
float_data: 0.0
name: "eqp2p_m1_0_Maxcst1"
}
initializer {
dims: 1
data_type: 1
float_data: -2.0
name: "eqp_ns0_Mulcst"
}
initializer {
dims: 1
dims: 1
data_type: 1
float_data: 25.094013214111328
name: "dec_Divcst"
}
initializer {
dims: 1
data_type: 1
float_data: 0.5
name: "dec_Addcst"
}
initializer {
dims: 1
data_type: 7
int64_data: -2
name: "predict_Mulcst"
}
initializer {
dims: 1
data_type: 7
int64_data: 1
name: "predict_Addcst"
}
input {
name: "X"
type {
tensor_type {
elem_type: 1
shape {
dim {
}
dim {
dim_value: 2
}
}
}
}
}
output {
name: "label"
type {
tensor_type {
elem_type: 7
shape {
dim {
}
dim {
dim_value: 1
}
}
}
}
}
output {
name: "scores"
type {
tensor_type {
elem_type: 1
shape {
dim {
}
dim {
dim_value: 1
}
}
}
}
}
}
opset_import {
domain: "ai.onnx.ml"
version: 2
}
opset_import {
domain: ""
version: 15
}
The last line shows the opsets. Let’s extract it.
domains = onx.opset_import
for dom in domains:
print("domain: %r, version: %r" % (dom.domain, dom.version))
Out:
domain: 'ai.onnx.ml', version: 2
domain: '', version: 15
There are two opsets, one for standard operators, the other for machine learning operators.
ONNX and opset¶
The converter can convert a model to an older opset than the default one, from 1 to the last available one.
def get_domain_opset(onx):
domains = onx.opset_import
res = [{'domain': dom.domain, 'version': dom.version}
for dom in domains]
return {d['domain']: d['version'] for d in res}
for opset in range(1, onnx_opset_version() + 1):
try:
onx = to_onnx(model, X[:1].astype(numpy.float32), target_opset=opset)
except RuntimeError as e:
print('target: %r error: %r' % (opset, e))
continue
nodes = len(onx.graph.node)
print('target: %r --> %s %d' % (opset, get_domain_opset(onx), nodes))
Out:
target: 1 error: RuntimeError("Operator 'OnnxReshape_5': requested version 1 < 5 schema version.")
target: 2 error: RuntimeError("Operator 'OnnxReshape_5': requested version 2 < 5 schema version.")
target: 3 error: RuntimeError("Operator 'OnnxReshape_5': requested version 3 < 5 schema version.")
target: 4 error: RuntimeError("Operator 'OnnxReshape_5': requested version 4 < 5 schema version.")
target: 5 --> {'ai.onnx.ml': 2, '': 5} 91
target: 6 --> {'ai.onnx.ml': 2, '': 6} 91
target: 7 --> {'ai.onnx.ml': 2, '': 7} 91
target: 8 --> {'ai.onnx.ml': 2, '': 8} 91
target: 9 --> {'ai.onnx.ml': 2, '': 9} 91
target: 10 --> {'ai.onnx.ml': 2, '': 10} 91
target: 11 --> {'ai.onnx.ml': 2, '': 11} 91
target: 12 --> {'': 12, 'ai.onnx.ml': 2} 91
target: 13 --> {'ai.onnx.ml': 2, '': 13} 91
target: 14 --> {'ai.onnx.ml': 2, '': 14} 91
target: 15 --> {'ai.onnx.ml': 2, '': 15} 91
/var/lib/jenkins/workspace/onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/skl2onnx/common/_topology.py:1405: UserWarning: Parameter target_opset 16 > 15 is higher than the the latest tested version.
warnings.warn(
target: 16 error: RuntimeError("The model is using version 16 of domain '' not supported yet by this library. You need to specify target_opset={'': 15}.")
It shows that the model cannot be converted for opset below 5. Operator Reshape changed in opset 5: a parameter became an input. The converter does not support opset < 5 because runtimes usually do not.
Other opsets¶
The previous example changed the opset of the main domain
''
but the other opset domain can be changed as well.
for opset in range(9, onnx_opset_version() + 1):
for opset_ml in range(1, 3):
tops = {'': opset, 'ai.onnx.ml': opset_ml}
try:
onx = to_onnx(
model, X[:1].astype(numpy.float32), target_opset=tops)
except RuntimeError as e:
print('target: %r error: %r' % (opset, e))
continue
nodes = len(onx.graph.node)
print('target: %r --> %s %d' % (opset, get_domain_opset(onx), nodes))
Out:
target: 9 error: RuntimeError("This converter requires at least opset 2 for domain 'ai.onnx.ml'.")
target: 9 --> {'ai.onnx.ml': 2, '': 9} 91
target: 10 error: RuntimeError("This converter requires at least opset 2 for domain 'ai.onnx.ml'.")
target: 10 --> {'ai.onnx.ml': 2, '': 10} 91
target: 11 error: RuntimeError("This converter requires at least opset 2 for domain 'ai.onnx.ml'.")
target: 11 --> {'ai.onnx.ml': 2, '': 11} 91
target: 12 error: RuntimeError("This converter requires at least opset 2 for domain 'ai.onnx.ml'.")
target: 12 --> {'': 12, 'ai.onnx.ml': 2} 91
target: 13 error: RuntimeError("This converter requires at least opset 2 for domain 'ai.onnx.ml'.")
target: 13 --> {'ai.onnx.ml': 2, '': 13} 91
target: 14 error: RuntimeError("This converter requires at least opset 2 for domain 'ai.onnx.ml'.")
target: 14 --> {'ai.onnx.ml': 2, '': 14} 91
target: 15 error: RuntimeError("This converter requires at least opset 2 for domain 'ai.onnx.ml'.")
target: 15 --> {'ai.onnx.ml': 2, '': 15} 91
/var/lib/jenkins/workspace/onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/skl2onnx/common/_topology.py:1405: UserWarning: Parameter target_opset 16 > 15 is higher than the the latest tested version.
warnings.warn(
target: 16 error: RuntimeError("This converter requires at least opset 2 for domain 'ai.onnx.ml'.")
target: 16 error: RuntimeError("The model is using version 16 of domain '' not supported yet by this library. You need to specify target_opset={'': 15}.")
Total running time of the script: ( 0 minutes 21.178 seconds)