fuel | The easiest HTTP networking library for Kotlin/Android | HTTP library
kandi X-RAY | fuel Summary
kandi X-RAY | fuel Summary
The easiest HTTP networking library for Kotlin/Android. You are looking at the documentation for 2.x.y.. If you are looking for the documentation for 1.x.y, checkout the 1.16.0 README.md.
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fuel Key Features
fuel Examples and Code Snippets
Lines of Code : 26
License : No License
Copypublic static int minRefuelStops(int target, int startFuel, int[][] stations) {
PriorityQueue pq = new PriorityQueue<>((f1, f2) -> Integer.compare(f2, f1));
int stops = 0, totalFuel = startFuel, prev = 0;
for (int[] s @BeforeMapping
protected void enrichDTOWithFuelType(Car car, @MappingTarget CarDTO carDto) {
if (car instanceof ElectricCar)
carDto.setFuelType(FuelType.ELECTRIC);
if (car instanceof BioDieselCar)
carDto.se @Override
String getInformation() {
return new StringBuilder("Fuel Car")
.append("\nFuel type: " + fuel)
.toString();
} Community Discussions
Trending Discussions on fuel
QUESTION
I am really new in big data analysing. Let's say I have a big data with the following features. I want to visualise the the percentage of missing values (None values) of fuel parameters for every id in specific hour. I want to draw a chart that x-axis is the time series (time column), y-axis is the 'id' and the colour will indicate its missing fuel percentage. I grouped the data base on 'id' and 'hour'
I don't know how to visualise missing value in a good way for all ids. For example if the percentage of missing value fuel of specific id in specific hour is 100% then the colour in that specific time and for that 'id' can be gray. If percentage of missing value in fuel is 50%, the colour can be light green. If percentage of missing value in fuel is 0% then the colour can be dark green. The colour must be based to the percentage of missing value in fuel, after grouping based on id and time.
...ANSWER
Answered 2022-Mar-25 at 09:39There is no right answer concerning missing values visualization, I guess it depends on your uses, habits ...
But first, to make it works, we need to preprocess your dataframe and make it analyzable, aka ensure its dtypes.
First let's build our data :
QUESTION
I am in need of help proving that an algorithm has greedy choice property and optimal substructure.
Context of the problem:
Consider a problem where a company owns n gas stations that are connected by a highway.
Each gas station has a limited supply g_i of gas-cans. Since the company don't know which gas station is most visited they want all of them to have the same amount of gas.
So they hire a fueling-truck to haul gas between the stations in their truck. However, truck also consumes 1 gas-can per kilometer driven.
Your task will be to help the chain calculate the largest amount of gas-cans g_bar
they can have at all Stations.
Consider the example: Here we have g = (20, 40, 80, 10, 20) and
p = (0, 5, 13, 33, 36) (in kilometers). In order to send one gas-can from station 3 to
station 4 we need to put 41 gas-cans in the truck, as the fueling-truck will consume 40 before reaching their destination (to send two gas-cans we need to put 42 in the truck).
The optimal g_bar for the example is 21 and can be achieved as follows:
Station 2 sends 11 gas-cans towards Station 1. One gas-can arrives while ten are consumed on the way.
Station 3 sends 59 gas-cans towards Station 4. 19 arrive while 40 are consumed on the way.
Station 4 now has 29 gas-cans and send eight towards Station 5. Two of these arrive and six are consumed on the way.
The final distribution of gas-cans is: (21, 29, 21, 21, 22).
Given an integer g_bar. Determine whether it is possible to get at least g_bar gas-cans in every Gas Station.
in order for the greedy choice property and optimal substructure to make sense for a decision problem, you can define an optimal solution to be a solution with at least g_bar gas-cans in every gas station if such a solution exists; otherwise, any solution is an optimal solution.
Input: The position p_i and gas-can supply g_i of each bar. Here g_i is the supply for the bar at position p_i. You may assume that the positions are in sorted order – i.e. p_1 < p_2 < . . . < p_n.
Output: The largest amount g_bar, such that each gas-station can have a gas-can supply of at least g_bar after the truck have transferred gas-cans between the stations.
How can i prove Greedy Choice and Optimal Substructure for this?
...ANSWER
Answered 2022-Mar-20 at 06:03Let's define an optimal solution: Each station has at least X gas cans in each station (X = g_bar).
Proving greedy property
Let us assume our solution is sub-optimal. There must exist a station i such that gas[i] < X. Based on our algorithm, we borrow X - gas[i] from station i+1 (which is a valid move, since we had already found a solution). Now station i has gas = X. This contradicts the original assumption that there must exist a station i such that gas[i] < X, which means our solution isn't suboptimal. Hence, we prove the optimality.
Proving optimal substructure
Assume we have a subset of the stations of size N', and our solution is suboptimal. Again, if the solution is suboptimal, there must exist a station i such that gas[i] < X. You can use the greedy proof again to prove that our solution isn't suboptimal. Since we have optimal solution for each arbitrary subset, we prove that we have optimal substructure.
QUESTION
I'm building my project with Vue.js 3, Vite.js. The app works fine when in dev mode (when using the dev server). Once I do launch the build command, Vite creates for me the /dist directory containing the build for my app. If I run the preview command (vite preview) it starts with no problem the preview of my build.
The problem is with some images which are coming from Vue components. All the images of my project are in the src/assets directory.
...ANSWER
Answered 2022-Jan-24 at 11:27Instead of using relative path (..) to the assets folder, you can use @/assets from any of the vue components to refer to files in the assets folder.
E.g this should work, no matter how deep the Vue component is nested.
QUESTION
Question:
Given a tree with N nodes.
Each edges of the tree contains:
D: the length of the edgeT: the gold needed to pay to go through that edge (the gold should be paid before going through the edge)
When moving through an edge, if you're carrying X golds, you will need X*D fuel.
There are 2 types of queries:
- u, v: find the fuel needed to transfer
Ggolds from u to v (Gis fixed among all queries) - u, v, x: update
Tof edge{u,v}to x ({u, v}is guaranteed to be in the tree)
Constraints:
...ANSWER
Answered 2022-Jan-17 at 11:03I told in the comments that a Dijkstra algorithm was necessary, but thinking better the DFS is really enough because there is only one path for each pair of vertices, we will always need to go from the starting point to the endpoint.
Using a priority queue instead of a stack would only change the order that the graph is explored, but in the worst case it would still visit all the vertices.
Using a queue instead of a stack would make the algorithm a breadth first search, again would only change the order in which the graph is explored.
Assuming that the number of nodes in a given distance increases exponentially with the threshold. An improvement for the typical case could be achieved by doing two searches and meeting in the middle. But only a constant factor.
So I think it is better to go with the simple solution, implementing this in C/C++ will result in a program dozens of times faster.
SolutionPrepare adjacency lists, and also makes the graph undirected
QUESTION
I have a table:
...ANSWER
Answered 2021-Dec-28 at 03:34You can simply use df.loc for that purpose
QUESTION
I am looking at the following website: https://data.gov.sg/dataset/bunker-sales-monthly?resource_id=44da3191-6c57-4d4a-8268-8e2c418d4b43 and they have the following example for extracting data using their API:
...ANSWER
Answered 2021-Dec-16 at 15:26Try pd.json_normalize:
QUESTION
This is my code:
...ANSWER
Answered 2021-Nov-24 at 09:49At h5 element you should control margin-bottom, the heading elements in bootstrap have some default values.
Try something like this:
QUESTION
I'm trying to make an update and I managed to do it in the firebase, but in the store is not updating. Here is my code
...ANSWER
Answered 2021-Nov-21 at 03:07Can you try to change your EDIT_CAR mutation to:
QUESTION
I am doing web scraping in python using requests of the following website.
Here in inspect element when you will load the data of any fuel type, you will see a API being triggered by name getFilteredInventery.
Suppose, you have selected Diesel, then I want to scrape all the data which is visible in response section of given API. But I cannot simply open it. In headers section you will see request payload and I think I have send it too as parameter.
I surfed internet and I found out that I can send request payload as param in requests.
This is my code:
...ANSWER
Answered 2021-Oct-28 at 09:43You variant:
QUESTION
Here is my sample data:
...ANSWER
Answered 2021-Oct-06 at 06:46You predict Year using Highway_mpg and City_mpg. If you want result like that blue table above and also, since you focus on average fuel consumption for each car model year, you'd better try this way.
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Vulnerabilities
No vulnerabilities reported
Install fuel
Fuel requests can be made on the Fuel namespace object, any FuelManager or using one of the String extension methods. If you specify a callback the call is async, if you don't it's blocking.
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