I have a FEN position and I want to analyze which position is stronger.
For example, I have this position
rnbq1bnr/pp2k2N/8/2p1p2Q/4N3/8/PPPP1PPP/R1B1KB1R b KQ - 0 1
How to evaluate a position and get score value using Stockfish? (example, the white score is +9 or black -5)
With Python you could use the python-chess library:
import chess
import chess.engine
engine = chess.engine.SimpleEngine.popen_uci("stockfish")
board = chess.Board("rnbq1bnr/pp2k2N/8/2p1p2Q/4N3/8/PPPP1PPP/R1B1KB1R b KQ - 0 1")
info = engine.analyse(board, chess.engine.Limit(depth=20))
print("Score:", info["score"])
# Score: #+9
engine.quit()
Take a look at the engine module for further details.
Related
I have lat/long data of two animals tracked in Western Australia and I'd like to find their home ranges using adehabitatHR.
library(sp)
library(rgdal)
library(raster)
library(adehabitatHR)
library(sf)
quolls<-read.csv("quolls.csv")
head(quolls)
Latitude Longitude animal_ID
1 -22.62271 117.1247 1
2 -22.62286 117.1246 1
3 -22.62192 117.1223 1
4 -22.62021 117.1224 1
5 -22.61989 117.1244 1
6 -22.62022 117.1260 1
But the home range estimates of each animal are obviously too small.
I think the EPSG must be wrong but after a very long time looking I still can't find the right one.
Can anyone point me in the right direction please?
# make a SpatialPoints dataframe without a CRS
quolls2 <- quolls
quoll.latlong<-data.frame(x=quolls2$Longitude,y=quolls2$Latitude)
coordinates(quolls2) <- quoll.latlong
# add crs
proj4string(quolls2) <- CRS(SRS_string = "EPSG:4283")
mcp<-mcp(quolls2[,7],percent=95,unout = c("ha"))
mcp
Home range for animal 1 is 1.217428e-08 and animal 2 is 6.253689e-08.
And likewise with kernel density estimation;
quoll_ud <- adehabitatHR::kernelUD(quolls2[7],grid = 450)
quoll_hr <- adehabitatHR::getverticeshr(quoll_ud, 99)
print(quoll_hr)
which estimates animal 1 at 2.36917592701502e-08 and animal 2 at 1.16018636413173e-07.
Just stumbled across the answer.. it's EPSG 28350.
I got it to work in the end by abandoning the raw lats and longs and instead importing a shapefile I had of the animal data with st_read.
Then st_transform to 28350.
Then as mcp accepts only SpatialPoints, I converted the object with
as(obj, "Spatial").
You’re given a chess board with dimension n x n. There’s a king at the bottom right square of the board marked with s. The king needs to reach the top left square marked with e. The rest of the squares are labeled either with an integer p (marking a point) or with x marking an obstacle. Note that the king can move up, left and up-left (diagonal) only. Find the maximum points the king can collect and the number of such paths the king can take in order to do so.
Input Format
The first line of input consists of an integer t. This is the number of test cases. Each test case contains a number n which denotes the size of board. This is followed by n lines each containing n space separated tokens.
Output Format
For each case, print in a separate line the maximum points that can be collected and the number of paths available in order to ensure maximum, both values separated by a space. If e is unreachable from s, print 0 0.
Sample Input
3
3
e 2 3
2 x 2
1 2 s
3
e 1 2
1 x 1
2 1 s
3
e 1 1
x x x
1 1 s
Sample Output
7 1
4 2
0 0
Constraints
1 <= t <= 100
2 <= n <= 200
1 <= p <= 9
I think this problem could be solved using dynamic-programing. We could use dp[i,j] to calculate the best number of points you can obtain by going from the right bottom corner to the i,j position. We can calculate dp[i,j], for a valid i,j, based on dp[i+1,j], dp[i,j+1] and dp[i+1,j+1] if this are valid positions(not out of the matrix or marked as x) and adding them the points obtained in the i,j cell. You should start computing from the bottom right corner to the left top, row by row and beginning from the last column.
For the number of ways you can add a new matrix ways and use it to store the number of ways.
This is an example code to show the idea:
dp[i,j] = dp[i+1,j+1] + board[i,j]
ways[i,j] = ways[i+1,j+1]
if dp[i,j] < dp[i+1,j] + board[i,j]:
dp[i,j] = dp[i+1,j] + board[i,j]
ways[i,j] = ways[i+1,j]
elif dp[i,j] == dp[i+1,j] + board[i,j]:
ways[i,j] += ways[i+1,j]
# check for i,j+1
This assuming all positions are valid.
The final result is stored in dp[0,0] and ways[0,0].
Brief Overview:
This problem can be solved through recursive method call, starting from nn till it reaches 00 which is the king's destination.
For the detailed explanation and the solution for this problem,check it out here -> https://www.callstacker.com/detail/algorithm-1
I have taken a column of dataset which has description in text form for each row. I am trying to find words with tf-idf greater than some value n. but the code gives a matrix of scores how do I sort and filter the scores and see the corresponding word.
tempdataFrame = wineData.loc[wineData.variety == 'Shiraz',
'description'].reset_index()
tempdataFrame['description'] = tempdataFrame['description'].apply(lambda
x: str.lower(x))
from sklearn.feature_extraction.text import TfidfVectorizer
tfidf = TfidfVectorizer(analyzer='word', stop_words = 'english')
score = tfidf.fit_transform(tempDataFrame['description'])
Sample Data:
description
This tremendous 100% varietal wine hails from Oakville and was aged over
three years in oak. Juicy red-cherry fruit and a compelling hint of caramel
greet the palate, framed by elegant, fine tannins and a subtle minty tone in
the background. Balanced and rewarding from start to finish, it has years
ahead of it to develop further nuance. Enjoy 2022–2030.
In the absence of a full data frame column of wine descriptions, the sample data you have provided is split in three sentences in order to create a data frame with one column named 'Description' and three rows. Then the column is passed to the tf-idf for analysis and a new data frame containing the features and their scores is created. The results are subsequently filtered using pandas.
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
doc = ['This tremendous 100% varietal wine hails from Oakville and was aged over \
three years in oak.', 'Juicy red-cherry fruit and a compelling hint of caramel \
greet the palate, framed by elegant, fine tannins and a subtle minty tone in \
the background.', 'Balanced and rewarding from start to finish, it has years \
ahead of it to develop further nuance. Enjoy 2022–2030.']
df_1 = pd.DataFrame({'Description': doc})
tfidf = TfidfVectorizer(analyzer='word', stop_words = 'english')
score = tfidf.fit_transform(df_1['Description'])
# New data frame containing the tfidf features and their scores
df = pd.DataFrame(score.toarray(), columns=tfidf.get_feature_names())
# Filter the tokens with tfidf score greater than 0.3
tokens_above_threshold = df.max()[df.max() > 0.3].sort_values(ascending=False)
tokens_above_threshold
Out[29]:
wine 0.341426
oak 0.341426
aged 0.341426
varietal 0.341426
hails 0.341426
100 0.341426
oakville 0.341426
tremendous 0.341426
nuance 0.307461
rewarding 0.307461
start 0.307461
enjoy 0.307461
develop 0.307461
balanced 0.307461
ahead 0.307461
2030 0.307461
2022â 0.307461
finish 0.307461
I have the start and end times of trips made by a bus, with the times in an Excel sheet. I want to make the graph as below :
I tried with Matlab nodes and graphs but did not got the exact figure, below is the Matlab code which I tried as an example:
A = [1 4]
B = [2 3]
weights = [5 5];
G = digraph(A,B,weights,4)
plot(G)
And the figure it generates:
I have got many more than 4 points in the Excel sheet, and I want them to all be displayed as in the first image.
Overview
You don't need any sort of complicated graph package for this, just use normal line plots! Here are methods in Excel and Matlab.
Excel
Give each bus stop a number, and list the bus stop number by the time it arrives/leaves there. I'll use stops number 0 and 1 for this example.
0 04:41
1 05:35
1 05:40
0 06:34
0 06:51
1 07:45
1 15:21
0 16:15
Then simply highlight the data and insert a "scatter with straight lines"
The rest is formatting. You can format the y-axis and tick "values in reverse order" to get the time increasing as in your desired plot. You can change the x-axis tick marks to just show integer stop numbers, get rid of the legend etc.
Final output:
Matlab
Here is the Matlab documentation for converting Excel formatted dates into Matlab datetime arrays: Convert Excel Date Number to Datetime.
Once you have the datetime objects, you can do this easily with the standard plot function.
% Set times up as a datetime array, could do this any number of ways
times = datetime(strcat({'1/1/2000 '}, {'04:41', '05:35', '05:40', '06:34', '06:51', '07:45', '15:21', '16:15'}, ':00'), 'format', 'dd/MM/yyyy HH:mm:ss');
% Set up the location of the bus at each of the above times
station = [0,1,1,0,0,1,1,0];
% Plot
plot(station, times) % Create plot
set(gca, 'xtick', [0,1]) % Limit to just ticks at the 2 stops
set(gca, 'ydir', 'reverse') % Reverse y axis to have earlier at top
set(gca,'XTickLabel',{'R', 'L'}) % Name the stops
Output:
Not sure how else to ask this but, I want to search for a term within several string elements. Here's what my code looks like (but wrong):
inplay = vector(length=nrow(des))
for (ii in 1:nrow(des)) {
if (des[ii] = 'In play%')
inplay[ii] = 1
else inplay[ii] = 0
}
des is a vector that stores strings such as "Swinging Strike", "In play (run(s))", "In play (out(s) recorded)" and etc. What I want inplay to store is a 1s and 0s vector corresponding with the des vector, with the 1s in inplay indicating that the des value had "In play%" in it and 0s otherwise.
I believe the 3rd line is incorrect, because all this does is return a vector of 0s with a 1 in the last element.
Thanks in advance!
The data.table package has syntax that is often similar to SQL. The package includes %like%, which is a "convenience function for calling regexpr". Here is an example taken from its help file:
## Create the data.table:
DT = data.table(Name=c("Mary","George","Martha"), Salary=c(2,3,4))
## Subset the DT table where the Name column is like "Mar%":
DT[Name %like% "^Mar"]
## Name Salary
## 1: Mary 2
## 2: Martha 4
The R analog to SQL's LIKE is just R's ordinary indexing syntax.
The 'LIKE' operator selects data rows from a table by matching string values in a specified column against a user-supplied pattern
> # create a data frame having a character column
> clrs = c("blue", "black", "brown", "beige", "berry", "bronze", "blue-green", "blueberry")
> dfx = data.frame(Velocity=sample(100, 8), Colors=clrs)
> dfx
Velocity Colors
1 90 blue
2 94 black
3 71 brown
4 36 beige
5 75 berry
6 2 bronze
7 89 blue-green
8 93 blueberry
> # create a pattern to use (the same as you would do when using the LIKE operator)
> ptn = '^be.*?' # gets beige and berry but not blueberry
> # execute a pattern-matching function on your data to create an index vector
> ndx = grep(ptn, dfx$Colors, perl=T)
> # use this index vector to extract the rows you want from the data frome:
> selected_rows = dfx[ndx,]
> selected_rows
Velocity Colors
4 36 beige
5 75 berry
In SQL, that would be:
SELECT * FROM dfx WHERE Colors LIKE ptn3
Something like regexpr?
> d <- c("Swinging Strike", "In play (run(s))", "In play (out(s) recorded)")
> regexpr('In play', d)
[1] -1 1 1
attr(,"match.length")
[1] -1 7 7
>
or grep
> grep('In play', d)
[1] 2 3
>
Since stringr 1.5.0, you can use str_like, which follows the structure of SQL's LIKE:
library(stringr)
fruit <- c("apple", "banana", "pear", "pineapple")
str_like(fruit, "app%")
#[1] TRUE FALSE FALSE FALSE
Not only does it include %, but also several other operators (see ?str_like).
Must match the entire string
_ matches a single character (like .)
% matches any number of characters (like .*)
% and _ match literal % and _
The match is case insensitive by default