Hi @sanjib!
Thanks for posting - just to confirm I got the same error:
# weights can also be an array, giving different mixing weights
# for each observation (first dimension must be number of components)
library(greta)
#>
#> Attaching package: 'greta'
#> The following objects are masked from 'package:stats':
#>
#> binomial, cov2cor, poisson
#> The following objects are masked from 'package:base':
#>
#> %*%, apply, backsolve, beta, chol2inv, colMeans, colSums, diag,
#> eigen, forwardsolve, gamma, identity, rowMeans, rowSums, sweep,
#> tapply
dim <- c(5, 4)
weights <- uniform(0, 1, dim = c(2, dim))
#> ℹ Initialising python and checking dependencies, this may take a moment.
#> ✔ Initialising python and checking dependencies ... done!
#>
b <- mixture(normal(1, 1, dim = dim),
normal(-1, 1, dim = dim),
weights = weights)
# While this code works for univariate normal, I failed to do so for two multivariate normal mixture. Here is a toy code
weights <- uniform(0,1,dim= c(2,10))
bb <- mixture(multivariate_normal(t(c(3,-3)), diag(1,2),n_realisations = 10),
multivariate_normal(t(c(-3,3)), diag(1,2),n_realisations = 10),
weights = weights)
#> Error in `initialize()`:
#> ! The dimension of weights must be either "2x1" or "2x"
#> but was ""
# produces the error
# Error in initialize():
# ! The dimension of weights must be either “2x1” or “2x”
# but was “”
# Run rlang::last_trace() to see where the error occurred.
# However, the code works if the same mixing weights are used for all realisations
weights <- uniform(0,1,dim= 2)
bb <- mixture(multivariate_normal(t(c(3,-3)), diag(1,2),n_realisations = 10),
multivariate_normal(t(c(-3,3)), diag(1,2),n_realisations = 10),
weights = weights)
bb
#> greta array <variable following a mixture distribution>
#> [,1] [,2]
#> [1,] ? ?
#> [2,] ? ?
#> [3,] ? ?
#> [4,] ? ?
#> [5,] ? ?
#> [6,] ? ?
#> [7,] ? ?
#> [8,] ? ?
#> [9,] ? ?
#> [10,] ? ?
#>
#> ℹ 10 more values
#> Use `print(n = ...)` to see more values
# I have tried various way to specify the weights and its dimensions but was not able to specify a model with different mixing weights for each observation.
Created on 2025-01-10 with reprex v2.1.1
Session info
sessioninfo::session_info()
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#>
#> ─ Python configuration ───────────────────────────────────────────────────────
#> python: /Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2/bin/python
#> libpython: /Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2/lib/libpython3.10.dylib
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#> version: 3.10.14 | packaged by conda-forge | (main, Mar 20 2024, 12:51:49) [Clang 16.0.6 ]
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#> numpy_version: 1.26.4
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#>
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This error message wasn’t quite complete, there was actually a bug in the code so I’ve fixed that up - see https://github.com/greta-dev/greta/issues/757
But essentially the weights needs to be 2x10x2 not 2x10:
library(greta)
weights <- uniform(0,1,dim= c(2,10, 2))
bb <- mixture(multivariate_normal(t(c(3,-3)), diag(1,2),n_realisations = 10),
multivariate_normal(t(c(-3,3)), diag(1,2),n_realisations = 10),
weights = weights)
Let me know if that helps?
