I am trying to add multiple robot instances and visualising their motions. I tried a couple of ways to do this and I ran into errors/issues. They are as follows:
I tried adding another model instance after the system is created.
parsers::urdf::AddModelInstanceFromUrdfFileToWorld(
FindResourceOrThrow("path/CompassGait.urdf"),
multibody::joints::kRollPitchYaw, tree.get());
parsers::urdf::AddModelInstanceFromUrdfFileToWorld(
FindResourceOrThrow("path/quadrotor.urdf"),
multibody::joints::kRollPitchYaw, tree.get());
As expected, there are two robots which are visible in the visualiser and there are 26 output ports in the system. But i am unable to visualise the required motion by the quadrotor. It seems to be following the x,y,z and roll pitch and yaw derivatives given as an input for the compass gait's output port. Is this an expected behaviour?
I experience a similar thing when I add 2 compass gait models and try to make them follow the same path. Even though I give the ports 14-27 the same inputs as i give to 0-13. The second robot is stuck near the origin while the first one moves fine as expected without any issues.
I needed some help or maybe some examples where I can get a better idea about visualising the motion for multiple robots.
[Updated] Please see note at the bottom.
drake::systems::DrakeVisualizer (which I assume you're using to publish your visualization messages) was designed to be connected to the state_output_port of drake::systems::RigidBodyPlant. According to the documentation for RigidBodyPlant,
The state vector, x, consists of generalized positions followed by generalized velocities.
That is, the generalized positions for all model instances come before the generalized velocities. When working with RigidBodyPlant and DrakeVisualizer this ordering is handled automatically.
From your question, however, I gather that you have separate, specialized systems for your quadrotor and compass-gait models (as per their respective examples). Each of these systems outputs its state as [q, v], where q is the generalized position vector and v is the generalized velocity vector. In that case you will need to use drake::systems::Demultiplexer and drake::systems::Multiplexer to split the outputs of the quadrotor and compass-gait systems and reassemble them in the required order:
+---------------+ +-------------+ q +-------------+
| | | +-------->+ |
| Compass-gait +-->+Demultiplexer| | |
| | | +-----+ v | |
+---------------+ +-------------+ | | |
+----->+ | +-----------------+
| | | | | |
| | | Multiplexer +-->+ DrakeVisualizer |
q| | | | | |
| +-->+ | +-----------------+
+---------------+ +-------------+ | | |
| | | +--+ | |
| Quadrotor +-->+Demultiplexer| | |
| | | +---+---->+ |
+---------------+ +-------------+ v +-------------+
Note: RigidBodyPlant and associated classes are being replaced by drake::multibody::MultibodyPlant and drake::geometry::SceneGraph. See run_quadrotor_lqr.cc for an example of using these new tools with a specialized plant.
Related
I am using the StarSpace embedding framework for the first time and am unclear on the "modes" that it provides for training and the differences between them.
The options are:
wordspace
sentencespace
articlespace
tagspace
docspace
pagespace
entityrelationspace/graphspace
Let's say I have a dataset that looks like this:
| Author | City | Tweet_ID | Tweet_contents |
|:-------|:-------|:----------|:-----------------------------------|
| A | NYC | 1 | "This is usually a short sentence" |
| A | LONDON | 2 | "Another short sentence" |
| B | PARIS | 3 | "Check out this cool track" |
| B | BERLIN | 4 | "I like turtles" |
| C | PARIS | 5 | "It was a dark and stormy night" |
| ... | ... | ... | ... |
(In reality, my dataset is not a language data and looks nothing like this, but this example demonstrates the point well enough.)
I would like to simultaneously create embeddings from scratch (not using pre-existing embeddings at any point) for each of the following:
Authors
Cities
Tweet/Sentences/Documents (EG. 1, 2, 3, 4, 5, etc.)
Words (EG. 'This', 'is', 'usually', ..., 'stormy', 'night', etc.)
Even after reading the coumentation, it doesn't seem clear which 'mode' of starspace training I should be using.
If anyone could help me understand how to interpret the modes to help select the appropriate one, that would be much appreciated.
I would also like to know if there are conditions under which the embeddings generated using one of the modes above, would in some way be equivalent to the embeddings built using a different mode (ignoring the fact that the embeddings would be different because of the non-determinstic nature of the process.)
Thank you
So lets say I have multiple probs, where one prob has two input DataFrames:
Input:
One constant stream of data (e.g. from a sensor) Second step: Multiple streams from multiple sensors
> df_prob1_stream1
timestamp | ident | measure1 | measure2 | total_amount |
----------------------------+--------+--------------+----------+--------------+
2019-09-16 20:00:10.053174 | A | 0.380 | 0.08 | 2952618 |
2019-09-16 20:00:00.080592 | A | 0.300 | 0.11 | 2982228 |
... (1 million more rows - until a pre-defined ts) ...
One static DataFrame of information, mapped to an unique identifier called ident, which needs to be assigned to the ident column in each df_probX_streamX in order to let the system recognize, that this data is related.
> df_global
ident | some1 | some2 | some3 |
--------+--------------+----------+--------------+
A | LARGE | 8137 | 1 |
B | SMALL | 1234 | 2 |
Output:
A binary classifier [0,1]
So how can I suitable train XGBoost to be able to make the best usage of one timeseries DataFrame in combination with one static DataFrame (containg additional context information) in one prob? Any help would be appreciated.
I have a dataset where some features are numerical, some categorical, and some are strings (e.g. description). To give an example, lets say I have three features:
| Number | Type | Comment |
---------------------------------------------------------
| 1.23 | 1 | Some comment, up to 10000 characters |
| 2.34 | 2 | Different comment many words |
...
Can I have all of them as input to a multi-layer network in dl4j, where numerical and categorical would be regular input features, but string comment feature will be processed first as word-series by a simple RNN (e.g. Embedding -> LSTM)? In other words, architecture should look something like this:
"Number" "Type" "Comment"
| | |
| | Embedding
| | |
| | LSTM
| | |
Main Multi-Layer Network
|
Dense
|
...
|
Output
I think in Keras this can be achieved by Concatenate layer. Is there something like this in DL4J?
Dl4j has 99% keras import coverage. We have concatneate layers as well. Take a look at the various vertices. Whatever you can do in keras should be do able in dl4j, save for very specific cases. More here: https://deeplearning4j.org/docs/latest/deeplearning4j-nn-computationgraph You want a MergeVertex.
I have many applications which I want to test, which have a largely overlapping set of features. Here is an oversimplified example of a scenario I might have:
Given <name> is playing a game,
When they shoot at a <color> target
Then they should <event>
Examples:
| name | color | event |
| Alice | red | hit |
| Alice | blue | miss |
| Bob | red | miss |
| Bob | blue | hit |
| Bob | green | hit |
It's a silly example, but suppose really I have a lot of players with different hit/miss conditions, and I want to run just the scenarios for a given name? Say, I only want to run the tests for Alice. There's still advantage to having all the hit/miss tests in a single Scenario Outline (since, after all, they're all closely related).
One approach would be to just duplicate the test for every name and tag them, so something like:
#Alice
Given Alice is playing a game
When she shoots at a <color> target
Then she should <event>
Examples:
| color | event |
| red | hit |
| blue | miss |
This way I can run behave --tags #Alice, But then I'm repeated the same scenario for every user, and that's a lot of duplication. Is there a good way to still compress all the examples into one scenario - but only selectively run some of them? What's the right approach here?
Version 1.2.5 introduced better ways to distinguish scenario outlines. It is now possible to uniquely distinguish them and thus select a unique scenario generated from an outline with --name= at the command line. For instance, suppose the following feature file:
Feature: test
Scenario Outline: test
Given <name> is playing a game,
When they shoot at a <color> target
Then they should <event>
Examples:
| name | color | event |
| Alice | red | hit |
| Alice | blue | miss |
| Bob | red | miss |
| Bob | blue | hit |
| Bob | green | hit |
Let's say I want to run only the test for Bob, red, miss. It is in the first table, 3rd row. So:
behave --name="#1.3"
will select this test. In version 1.2.5 and subsequent versions. A generated scenario gets a name which includes "#<table number>.<row number>" where <table number> is the number of the table (starting from 1) and <row number> is the number of the row.
This won't easily allow you to select all scenarios that pertain to a single user. However, you can achieve it in another way. You can split your examples in two:
Examples: Alice
| name | color | event |
| Alice | red | hit |
| Alice | blue | miss |
Examples: Bob
| name | color | event |
| Bob | red | miss |
| Bob | blue | hit |
| Bob | green | hit |
The table names will appear in the generated scenario names and you could ask behave to run all the tests associated with one table:
behave --name="Alice"
I do not know of a way to access the example name in steps and thus get rid of the first column.
The full set of details is in the release notes for 1.2.5.
I resolved the following question but
I have another issue. I would like to
analyse "Likert Scale" questionaire
which is measured 1 to 5 ( agree,
strongly agree etc ). I tried many
ways but I didn't combine all results.
Have you got any idea to analyze
likert scale?
Does anybody help us to define following type of question in SPSS variable view?
( looks like array question, user answers non unique which they can enter text )
QUESTION 1:
Allows a table of text inputs
+----------------------------------------------+
| Speed Design Accuracy |
+----------+---------+----------+--------------+
| Google | | | |
+----------+---------+----------+--------------+
| Yahoo | | | |
+----------+---------+----------+--------------+
| Bing | | | |
+----------+---------+----------+--------------+
I had the same problem. Fortunately, it is easy to solve ;)
If you have your data in the table - you have to "restructure" it (Menu - Data - Restructure). This option allows you to create multidimansional variables. You can find some tutorial on youtube for data restructuring.
In your case, you have to make it manually. You just repeat your identifying variable accordng to the amount of likert scale questions. Let's assume you have 3 questions to "Speed", 3 questions to "Design", and 3 questions to "Accuracy". Your table should look like this:
+----------------------------------------------+
| Speed1 Speed2 Speed3 | Duration1 | Duration2 | ...
+----------+---------+----------+--------------+
| Google | | | | | |
+----------+---------+----------+--------------+
| Yahoo | | | |
+----------+---------+----------+--------------+
| Bing | | | |
+----------+---------+----------+--------------+
You can restructure the data later to perform statistical analysis.
In the case of repeated measurement (e.g. you asked your Likert scale question in the same company 3 times over time), your table might look like this:
+----------------------------------------------+
| Speed1 Speed2 Speed3 | Duration1 | Duration2 | ...
+----------+---------+----------+--------------+
| Google | | | | | |
+----------+---------+----------+--------------+
| Google | | | |
+----------+---------+----------+--------------+
| Google | | | |
+----------+---------+----------+--------------+
| Yahoo | | | |
+----------+---------+----------+--------------+
| Yahoo | | | |
+----------+---------+----------+--------------+
| Yahoo | | | |
+----------+---------+----------+--------------+
| Bing | | | |
+----------+---------+----------+--------------+
...
I hope, it helped!
Best,
Eugene
I am not sure I know what you are asking, but I believe you are looking for some guidance as to what the "dataset" might need to look like. If you run the following syntax, you should get a better idea of how I would structure it.
DATA LIST LIST (",") / browser (A30) type (A30) score.
BEGIN DATA
Google, Speed, 123
Yahoo, Speed, 34
Bing, Design, 23
Google, Accuracy, 231
Yahoo, Design, 12
END DATA.
Likert scale data should be analyzed using non-parametric methods. Two ways to handle this.
1). Rank the cases and then perform ANOVA on the ranked values
2). Perform Kruskal Wallis on the Likert scale data
Regards