forecaster | Forecaster is a library
kandi X-RAY | forecaster Summary
kandi X-RAY | forecaster Summary
Forecaster is a library for manipulating and casting associative arrays in PHP. While the project makes use of a class and helpers found in Laravel it can be used in non-Laravel projects as it only depends upon the Tighten Co's Collect package and not the Laravel Support package meaning it's compatible with any framework or stand-alone project.
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Top functions reviewed by kandi - BETA
- Cast all values of an array .
- Cast value to a given type .
- Cast primitive values .
- Cast array items .
- Get processed instance .
- Define a custom transformation .
- Cast value .
- Run a condition with a condition .
- Return an instance of the given class .
- Make a new collection item
forecaster Key Features
forecaster Examples and Code Snippets
Lines of Code : 101License : Permissive (MIT)
Copy$result = forecast([
'a-string-int' => '10',
'a-string-float' => '1.5',
'an-int' => 10,
'another-int' => 1,
'do-not-touch' => '11'
])
->cast('a-string-int', 'anInt', 'int')
->cast('a-string-float', 'aF $processed = Forecaster::make([
'test' => '10',
])
->when(true, function (Forecaster $caster) {
$caster->cast('test', 'output', 'int');
})
->get();
// results to
[
'output' => 10,
]
$processed = $collection = collect([
['test' => '123.456'],
['test' => '789.101112']
])
->forecast(function (Forecaster $forecast) {
$forecast->cast('test', 'output', 'float');
})
->toArray();
// results to
[
[ Community Discussions
Trending Discussions on forecaster
QUESTION
I have various points saved in a GeoDataFrame gdf.
I want to plot these points on an image (of the world), that comes with matplotlib via stock_img().
I thought this would be straight forward, but I only get one point plotted on the image, not all of them (there should be 105).The data can be downloaded here https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Introduction+to+Geospatial+Data+in+Python/florence.csv
Reproducible example:
...ANSWER
Answered 2022-Mar-01 at 00:12This is working for me.
QUESTION
My conditions are as follows :
- If the value of userIncome is entered more than 400000, show error.
- Similarly, if the value is less than 500, show error.
Repeat the loop until a correct value is entered.
...ANSWER
Answered 2022-Feb-15 at 17:29For starters this named constant
QUESTION
I have the following dataframe called "lastDays":
...ANSWER
Answered 2021-Aug-06 at 19:51I was able to find the solution that was the following:
Apply pd.date-range to create a new column of dates in the created dataframe "nextMonth" and reflect the forecasts. The applied function nextMonth['Date'] = pd.date_range (start = lastDays.index [-1], period = len (lastDays), freq = "H") starts the last day of the original dataframe "lastDays" for append the subsequent hours and days based on the length of the original dataframe, which joins the forecast results from day + 1 of the last date of the dataframe originates "lastDays".
Finally, what I did was apply apend to add the dataframe built with the forecasts "nextMonth" to the original dataframe "lastDays" lastDays.append (nextMonth), obtaining the expected results.
I don't know if it will be the most optimal solution but it is fully functional for me and meets the expectations of my project.
QUESTION
I'm developing a large CRA single page app. It has been running fine for months, with the normal bugs that are normally fixable. A couple of weeks ago it failed by just hanging in the splash page with a spinning circle. No code was hit so no breakpoints worked. I did a lot of experimentation with the importing and exporting of all the files, thinking that was the root cause. Finally I thought to do a build and run that. That did get past the splash screen and generate an exception. The first was fixed, but now I get this Cannot access 'calculationEntry' before initialization error. I'm using VSCode and the launch.json configuration is:
...ANSWER
Answered 2021-May-30 at 20:54The problem exists because the function was declared as an arrow function. So it is declared but not initialized. The compilation phase is hoisting this uninitialized variable in the temporal dead zone which causes the TDZ exception. Changing the function declaration to "function calculationEntry() {...}" solves the problem.
QUESTION
Very new to dapr and docker.
I followed along the dapr getting started. The simple hello world state management example worked fine. Yes Bruce, we all know you are Batman.
So next I built the weather forecast multi-container example for .NET Core. This worked beautifully. (I named my front-end razor pages "wxui" and the back-end webapi "wxapi").
Finally, I wanted to try my hand at adding state management to the weather forecaster example. I modified the front-end Razor Pages app to store and retrieve a bit of state and added a redis container to my docker-compose file.
Things are not going well.
The wxui-dapr container is exiting with this message:
time="2021-05-20T22:47:50.3179068Z" level=fatal msg="process component statestore error: redis store: error connecting to redis at localhost:6379: dial tcp 127.0.0.1:6379: connect: connection refused" app_id=wxui instance=69254f9724b0 scope=dapr.runtime type=log ver=1.1.2
I'm going to guess that the dapr sidecar container is not mapping local port 6379 to the redis container. But I have no idea how to test or fix that.
Here's my docker-compose.yml file, if that's useful:
...ANSWER
Answered 2021-May-21 at 19:29So in my limited environment, using my very limited understanding of docker networking, I was able to make it work. Please feel free to offer better solutions!
I ended up changing the docker-compose.yml file to give the redis container a hostname:
QUESTION
from sktime.forecasting.compose import ReducedRegressionForecaster
ANSWER
Answered 2021-Mar-18 at 11:28Thats because that doesn't exist.
https://github.com/alan-turing-institute/sktime/blob/master/sktime/forecasting/compose/__init__.py
Thats not exported by the librray. Perhaps you wanted
RegressionForcaster or RecursiveRegressionForecaster
QUESTION
I am trying to deploy a code of mine (that uses fbprophet) as an EC2 instance using Gitlab CI.
my requirements.txt file:
ANSWER
Answered 2020-Jul-29 at 10:05The issue with fbprophet is that it imports and executes its own code in setup.py. This is sometimes called a "race condition", although it has nothing to do with parallel code execution - one has to be careful about importing code which is being installed.
Here, fbprophet.models imported in setup.py triggers import of fbprophet.forecaster (even if unused) via fbprophet/__init__.py, which triggers the imports of numpy, pandas etc. None of the packages is declared as build dependency via setup_requires, thus the error. To avoid it, you have to install the dependencies in a separate command before installing your own requirements:
QUESTION
I am following the Pyro introductory tutorial in forecasting, and trying to access the learned parameters after training the model, I get different results using different access methods for some of them (while getting identical results for others).
Here is the stripped-down reproducible code from the tutorial:
...ANSWER
Answered 2020-May-10 at 02:14pyro.param() returns transformed parameters in this case to the positive reals for scales.
QUESTION
syntax = "proto3";
package gRPC_Forecaster;
import "google/protobuf/timestamp.proto";
service Forecaster {
rpc Forecast (ProductToForecast) returns (ForecastData) {}
}
message ProductToForecast {
int32 productID = 1;
google.protobuf.Timestamp startDate = 2;
int32 forecastDurationInWeeks = 3;
Model model = 4;
}
message ForecastData {
repeated int32 data = 1;
repeated int32 date = 2;
}
message Model {
oneof model_oneof {
ARIMA arima = 1;
SARIMA sarima = 2;
}
}
message ARIMA{
int32 p = 1;
int32 d = 2;
int32 q = 3;
}
message SARIMA{
int32 p = 1;
int32 d = 2;
int32 q = 3;
int32 p2 = 4;
int32 d2 = 5;
int32 q2 = 6;
}
ANSWER
Answered 2020-Mar-26 at 15:57For now I found a solution with the hasattr() function of Python.
So when I received a request from the PHP application I can do hasattr(request.model, "arima") and works. I am not certain this is the way to go.
QUESTION
I'm trying to use Keras to make simultaneous predictions for multiple variables. Using this example here, I want to predict values for all features including pm 2.5, DEWP, TEMP etc. instead of just pollution (pm 2.5). Essentially, this is, given all the variables, build a model to predict all variables as time series, not just forecasting one variable.
I modified the original sample code by using the reshaped 3D data, but got an error. Here's the code:
...ANSWER
Answered 2020-Mar-18 at 18:09I've found a solution here (under "Multiple Parallel Series"). We just need to reshape the features and labels and feed in the network, it'll just work! The features should have the shape of (n_steps, n_features) while the labels should have the shape (n_samples, n_features) (if we are predicting 1 timestep).
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