cellchat <- computeCommunProb(cellchat, type = "triMean",#type = "truncatedMean", trim = 0.1, distance.use = FALSE, interaction.range = 250, contact.knn = TRUE, contact.knn.k = 6) Error in if (sum(P1_Pspatial) == 0) { : missing value where TRUE/FALSE needed ...
python script or pipeline step. It shares the common computing context and most of the cases you can just directly convert the Spark Dataframe to Pandas and Dask Dataframe without persisting first to an intermediary storage.
|-- rng_bin() : The revised iterative discretization based on the equal-width range of X. |-- kmn_bin() : The discretization algorthm based on the kmean clustering of X. |-- gbm_bin() : The discretization algorthm based on the gradient boosting machine. |-- arb_bin() : The ...
RiskOptima is a comprehensive Python toolkit for evaluating, managing, and optimizing investment portfolios. This package is designed to empower investors and data scientists by combining financial risk analysis, backtesting, mean-variance optimization, and machine learning capabilities into a single, cohes...
>>>fromlexicalrichnessimportLexicalRichness# text example>>>text="""Measure of textual lexical diversity, computed as the mean length of sequential words ina text that maintains a minimum threshold TTR score.Iterates over words until TTR scores falls below a threshold, then increase factorcounter ...
And now, we calculate PSS using the empirical estimates of availability: pss.r(mf.samp,mf.phyl.cols,mf.q) In both cases, the most important values are under the "pss" column of the resulting dataframe. values close to zero indicate a random phylogenetic structure, negative values indicate ...
DataFrame are the best to keep track of the names of each data point.Let's consider the following preference matrix:Each row is a candidate and each column is a judge. Here is the results of rk.borda(matrix), computing the mean rank of each candidate:...
Speed test of different scores pd_df : shape - (1797, 65) total elements=116805 Columns types: pd_df.dtypes.value_counts() : 64 x float64 + 1 x Int32 score_index_ball_hall 5.06 ms ± 79.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) score_index_banfeld_Raf...
Why are ECI and PCI are both normalized using ECI's mean and std. dev? This normalization preserves the property that ECI = (mean of PCI of products for which MCP=1)