mortonjt

"Achimill" "Agrostol" "Airaprae" "Alopgeni" "Anthodor" "Bellpere" "Bromhord" "Ch
"1" 1 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 7 0 4 2 0 0 0 0 0 0 0 0 0 0
"2" 3 0 0 2 0 3 4 0 0 0 0 4 0 0 0 0 5 0 4 7 0 0 0 0 5 0 5 0 0 0
"3" 0 4 0 7 0 2 0 0 0 0 0 4 0 0 0 0 6 0 5 6 0 0 0 0 2 0 2 0 2 0
"4" 0 8 0 2 0 2 3 0 2 0 0 4 0 0 0 0 5 0 4 5 0 0 5 0 2 0 1 0 2 0
"5" 2 0 0 0 4 2 2 0 0 0 0 4 0 0 0 0 2 5 2 6 0 5 0 0 3 2 2 0 2 0
"6" 2 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 6 5 3 4 0 6 0 0 3 5 5 0 6 0
"7" 2 0 0 0 2 0 2 0 0 0 0 0 0 0 0 2 6 5 4 5 0 3 0 0 3 2 2 0 2 0
"8" 0 4 0 5 0 0 0 0 0 0 4 0 0 0 4 0 4 0 4 4 2 0 2 0 3 0 2 0 2 0
"9" 0 3 0 3 0 0 0 0 0 0 0 6 0 0 4 4 2 0 4 5 0 2 2 0 2 0 3 0 2 0

mortonjt

mortonjt

#!/usr/bin/env python


"""
Filter sequences from a biom table based on a fasta file
used for AG bloom filtering
"""

# amnonscript

mortonjt

import numpy as np
import patsy
import pandas as pd
from biom import Table

# This is the main function
def poisson_cat(table, metadata, category, ref=None):
    """ Poisson differential abundance.

    Parameters

mortonjt

## import modules
import os
import copy
import time
from tqdm import tqdm
import numpy as np
from skbio.stats.composition import clr_inv as softmax
from scipy.stats import spearmanr
import datetime
import pandas as pd

mortonjt

"""
Installation can be done with conda via

conda create -n textminer beautifulsoup4 numpy pandas scipy scikit-learn nltk lxml jupyter notebook matplotlib seaborn
"""

mortonjt

data {
  int<lower=0> N;    // number of samples
  int<lower=0> D;    // number of dimensions
  int<lower=0> p;    // number of covariates
  real depth[N];     // sequencing depths of microbes
  matrix[N, p] x;    // covariate matrix
  int y[N, D];       // observed microbe abundances
}

parameters {

mortonjt

data {
  int<lower=0> N;    // number of samples
  int<lower=0> D;    // number of dimensions
  int<lower=0> J;    // number of subjects
  int<lower=0> p;    // number of covariates
  real depth[N];     // sequencing depths of microbes
  matrix[N, p] x;    // covariate matrix
  int y[N, D];       // observed microbe abundances
  int<lower=1, upper=J> subj_ids[N];   // subject ids

mortonjt

import numpy as np
import matplotlib.pyplot as plt
from skbio.stats.ordination import pcoa
from skbio import DistanceMatrix

from scipy.spatial.distance import pdist, squareform

# embedding = < your language model embedding > # dim L x D where L is the length, D is the dimension
rr_dist = squareform(pdist(embedding))
dm = DistanceMatrix(rr_dists_d.mean(2))

mortonjt

import torch
from torch import nn
from torch.autograd import Variable

class AdaptiveSoftmax(nn.Module):
    def __init__(self, input_size, cutoff):
        super().__init__()
        
        self.input_size = input_size
        self.cutoff = cutoff