DarkSkySevenDay | Seven Day Weather Information from Dark Sky | Predictive Analytics library
kandi X-RAY | DarkSkySevenDay Summary
kandi X-RAY | DarkSkySevenDay Summary
Current and Seven Day Weather Information from Dark Sky. copyright 2020 - Jessica Hershey. This library will be obsolete as of OCTOBER 2020 DarkSky has been sold to Apple and is no longer accepting new API Key requests A NEW LIBRARY IS IN THE MAKING AND WILL BE AVAILABLE SOON. Google API Key / Dark Sky API Key / HTTPClient.h / ArduinoJson.h. Weekly Forecast provides weather data for NOW and 7 days in the future. Using your WiFi connection only (no GPS required) and your Google API Key, Weekly Forecast triangulates your location and then requests the future forecast from Dark Sky Weather with your Dark Sky API Key. No need to worry about setting up the http requests, filters, or finding your location. Let Weekly Forecast do it all for you. All you need to do is install the library as you would any other (clone, download, or use the Arduino Library Manager… highly suggested as dependency libraries are installed at the same time). Then simply call getWeather(“your google key”,”your dark sky key”) and sit back while weather information downloads to your device. DarkSkySevenDay forecast; //←------------ Invoke the library. VARIABLES for Current local weather conditions Examples follow the variable, all times are LONG in EPOCH format (GMT), decimal numbers are FLOATS forecast.current.dayTime; // 1582151288 forecast.current.summary; // "Clear" forecast.current.icon; // "clear-day" forecast.current.nearestStormDistance; // 50 forecast.current.nearestStormBearing; // 4 forecast.current.precipIntensity; // 0 forecast.current.precipProbability; // 0 forecast.current.temperature; // 46.38 forecast.current.apparentTemperature; // 41.49 forecast.current.dewPoint; // 17.18 forecast.current.humidity; // 0.31 forecast.current.pressure; // 1026.4 forecast.current.windSpeed; // 10.22 forecast.current.windGust; // 10.22 forecast.current.windBearing; // 348 forecast.current.cloudCover; // 0.02 forecast.current.uvIndex; // 0 forecast.current.visibility; // 10 forecast.current.ozone; // 323.4. -------------------------------------------------------- VARIABLES for Seven Day Forecast. Examples follow the variable, all times are LONG in EPOCH format (GMT), decimal numbers are FLOATS forecast.forecastDay[x].dayTime; // 1582088400 forecast.forecastDay[x].summary; // "Partly cloudy throughout the day." forecast.forecastDay[x].icon; // "partly-cloudy-day" forecast.forecastDay[x].sunriseTime; // 1582112760 forecast.forecastDay[x].sunsetTime; // 1582151880 forecast.forecastDay[x].moonPhase; // 0.89 forecast.forecastDay[x].precipIntensity; // 0.0009 forecast.forecastDay[x].precipIntensityMax; // 0.0028 forecast.forecastDay[x].precipIntensityMaxTime; // 1582105560 forecast.forecastDay[x].precipProbability; // 0.3 forecast.forecastDay[x].precipType; // "rain" forecast.forecastDay[x].temperatureHigh; // 51.24 forecast.forecastDay[x].temperatureHighTime; // 1582139280 forecast.forecastDay[x].temperatureLow; // 26.83 forecast.forecastDay[x].temperatureLowTime; // 1582199760 forecast.forecastDay[x].apparentTemperatureHigh; // 50.76 forecast.forecastDay[x].apparentTemperatureHighTime; // 1582139100 forecast.forecastDay[x].apparentTemperatureLow; // 19.9 forecast.forecastDay[x].apparentTemperatureLowTime; // 1582199940 forecast.forecastDay[x].dewPoint; // 26.79 forecast.forecastDay[x].humidity; // 0.54 forecast.forecastDay[x].pressure; // 1024.1 forecast.forecastDay[x].windSpeed; // 6.49 forecast.forecastDay[x].windGust; // 24.15 forecast.forecastDay[x].windGustTime; // 1582131600 forecast.forecastDay[x].windBearing; // 324 forecast.forecastDay[x].cloudCover; // 0.53 forecast.forecastDay[x].uvIndex; // 3 forecast.forecastDay[x].uvIndexTime; // 1582134180 forecast.forecastDay[x].visibility; // 10 forecast.forecastDay[x].ozone; // 322.6 forecast.forecastDay[x].temperatureMin; // 30.17 forecast.forecastDay[x].temperatureMinTime; // 1582174800 forecast.forecastDay[x].temperatureMax; // 51.24 forecast.forecastDay[x].temperatureMaxTime; // 1582139280 forecast.forecastDay[x].apparentTemperatureMin; // 24.39 forecast.forecastDay[x].apparentTemperatureMinTime; // 1582174800 forecast.forecastDay[x].apparentTemperatureMax; // 50.76 forecast.forecastDay[x].apparentTemperatureMaxTime; // 1582139100 Please have your Dark Sky Key, available at www.darksky.net and your Google API key available at As usual, if you have any questions just contact me and we’ll get you all on track. DISPLAY USE IDEA GNU General Public License v3.0. Permissions of this strong copyleft license are conditioned on making available complete source code of licensed works and modifications, which include larger works using a licensed work, under the same license. Copyright and license notices must be preserved. Contributors provide an express grant of patent rights.
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Trending Discussions on Predictive Analytics
QUESTION
GPU is good for parallel computing but the problem is some machine learning libraries don't utilize the GPU, unless that machine learning based on image processing or some sort of graphics processing, what if I am using machine learning for predictive Analytics? do libraries like TensorFlow utilize the GPU? or they use only CPU? or can I choose which processing unit to use? whats the deal here?
note: predictive Analysis requires no graphics processing.
...ANSWER
Answered 2020-Nov-21 at 21:35The computation that happens in the GPU in any of the machine learning frameworks that support GPUs is not limited to graphical processing. For instance, if your model is a simple logistic regression, a framework such as TensorFlow will run it on the GPU if properly configured.
The advantage of GPUs for machine learning is that training big neural networks benefits greatly from the high level of parallelism that the GPUs offer.
If you want to know more about this, I'd recommend you start here or here.
some things to consider:- how much a model will benefit from running in the GPU will depend on how much it will benefit from parallel computation in general.
- Deep Learning models can be applied to predictive analytics, as well as more classical machine learning models. Bear in mind that neural nets are possibly the category of models that will benefit inherently from the GPU (see links above).
- Even though running models using GPUs (or even more specialised hardware) can bring benefits, I would suggest that you don't choose a framework and, especially, don't choose an algorithm based solely on the fact that it will benefit from parallelism, but rather look at how appropriate a given algorithm is for the data you have.
QUESTION
I have a pandas dataframe which is a large number of answers given by users in response to a survey and I need to re-structure it. There are up to 105 questions asked each year, but I only need maybe 20 of them.
The current structure is as below.
What I want to do is re-structure it so that the row values become column names and the answer given by the user is then the value in that column. In a picture (from Excel), what I want is the below (I know I'll need to re-name my columns, but that's fine once I can create the structure in the first place):
Is it possible to re-structure my dataframe this way? The outcome of this is to use some predictive analytics to predict a target variable, so I need to re-strcture before I can use Random Forest, kNN, and so on.
...ANSWER
Answered 2020-Nov-01 at 19:39You might want try pivoting your table:
QUESTION
I have js files Dashboard and Adverts. I managed to get Dashboard to list the information in one json file (advertisers), but when clicking on an advertiser I want it to navigate to a separate page that will display some data (Say title and text) from the second json file (productadverts). I can't get it to work. Below is the code for the Dashboard and next for Adverts. Then the json files
...ANSWER
Answered 2020-May-17 at 23:55The new object to get params in React Navigation 5 is:
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