This is the original Dataframe: What I wanted : I wanted to convert this above data-frame into this multi-indexed column data-frame : I managed to do it by this piece of code : # tols : original dataframe cols = pd.MultiIndex.from_product([['A','B'],['Y','X'] ['P','Q']]) tols.set_axis(cols, axis = 1, inplace = False) What I tried : I tried to do this with the reindex method like this : cols = pd.MultiIndex.from_product([['A','B'],['Y','X'], ['P','Q']]) tols.reindex(cols, axis = 'columns') it resulted in an output like this …

It has been evaluated the use of learned piecewise segments in order to create compressed indexes that substitute classical B+-Tree structures, in order to optimize space and have higher query response speed. We propose, instead, that given $S= \{(k_1,1), (k_2,2), \dots, (k_n,n)\}$, to employ a function: $f(k) = a k^2 + b k + c$ where $k\in \mathbb{I}$, is the input key to be searched, and $f(k)$ will give an approximation of the proper index $i$ of $k$. It turns …

I have created a training dataframe Traindata as following: dataFile='/content/drive/Colab Notebooks/.../Normal_Anomalous_8Digits.csv' data8=pd.read_csv(dataFile) And Traindata looks like the following: Here Output is predicted variable which is not included in test data. Col1 Col2 Output 0 0.001655 0.464986 1 1 0.943110 0.902166 0 2 0.071235 0.674283 1 ... ... ... .. 1007 0.698048 0.058458 1 1008 0.289333 0.702763 1 1009 rows × 3 columns Now the model is trained as following commands: from pgmpy.models import BayesianModel, BayesianNetwork from pgmpy.estimators import MaximumLikelihoodEstimator model …

Is it possible to perform Book index searching using Machine learning algorithms? Inputs : 1 Book pages with page numbers as images. 2 Index words in the book. Output: Tracing the page number/s with the indexes provided.

The below code snippet shows how statsmodels seems to flatten MultiIndex tuples by joining them with an underscore "_". import numpy as np import pandas as pd from statsmodels.regression.linear_model import OLS K = 2 N = 10 ERROR_VOL = 1 np.random.seed(0) X = np.random.rand(N, K) coefs = np.linspace(0.1, 1, K) noise = np.random.rand(N) y = X @ coefs + noise * ERROR_VOL index_ = pd.MultiIndex.from_tuples([('some_var','feature_0'), ('some_var','feature_1')]) df = pd.DataFrame(X, columns=index_) ols_fit = OLS(y, df, hasconst=False).fit() print(ols_fit.params) The result is >>> …

I'm looking for an optimal way to search a large body of text for a combination of words that resemble any CITY, STATE combination I have in a separate CITY-STATE database. My only idea would be to do a separate search against the body of text for each CITY, STATE in the database, but that would require a lot of time considering the amount of CITY, STATE combinations the database has in it. The desired result from this query would …

I'm looking for a spatial index that can efficiently find the most extreme n points in a certain direction, i.e. for a given w, find x[0:n] in the dataset where x0 gives the largest value of w.x and x1 the second largest value of w.x, etc... . Is there a name for this type of query? What would be an efficient data structure to use? x might have around 20 dimensions. Thankyou!

I'm trying to build a nonparametric density function for a fairly large dataset that can be evaluated efficently, and can be updated efficiently when new points are added. There will only ever be a maximum of 4 independent variables, but we can start off with 2. Lets use a gaussian kernel. Let the result be a probability density function, i.e. its volume will be 1. In each evaluation, we can omit all points for which the evaluation point is outside …

As we all know, there are some data indexing techniques, using by well-known indexing apps, like Lucene (for java) or Lucene.NET (for .NET), MurMurHash, B+Tree etc. For a No-Sql / Object Oriented Database (which I try to write/play a little around with C#), which technique you suggest? I read about MurMurhash-2 and specially v3 comments say Murmur is very fast. Also Lucene.Net has good comments on it. But what about their memory footprints in general? Is there any efficient solution …