long-overdue tidy style reformat

jlmelville · Dec 11, 2023 · ba85ba8 · ba85ba8
1 parent 07b8a5d
commit ba85ba8
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Showing 24 changed files with 1,028 additions and 963 deletions.
diff --git a/R/affinity.R b/R/affinity.R
@@ -5,11 +5,9 @@ fuzzy_set_union <- function(X, set_op_mix_ratio = 1) {
   XX <- X * Matrix::t(X)
   if (set_op_mix_ratio == 0) {
     Matrix::drop0(XX)
-  }
-  else if (set_op_mix_ratio == 1) {
+  } else if (set_op_mix_ratio == 1) {
     Matrix::drop0(X + Matrix::t(X) - XX)
-  }
-  else {
+  } else {
     Matrix::drop0(
       set_op_mix_ratio * (X + Matrix::t(X) - XX) + (1 - set_op_mix_ratio) * XX
     )
@@ -33,12 +31,12 @@ smooth_knn <- function(nn_dist,
   }
   tsmessage(
     "Commencing smooth kNN distance calibration",
-    pluralize("thread", n_threads, " using"), appendLF = FALSE
+    pluralize("thread", n_threads, " using"),
+    appendLF = FALSE
   )
   if (length(target) == 1) {
-    tsmessage(" with target n_neighbors = ", formatC(2 ^ target), time_stamp = FALSE)
-  }
-  else {
+    tsmessage(" with target n_neighbors = ", formatC(2^target), time_stamp = FALSE)
+  } else {
     tsmessage(time_stamp = FALSE)
   }
   affinity_matrix_res <- smooth_knn_distances_parallel(
@@ -87,8 +85,7 @@ smooth_knn_matrix <- function(nn,
       target <- log2(n_nbrs + 1) * bandwidth
     }
     skip_first <- FALSE
-  }
-  else {
+  } else {
     nnt <- nn_graph_t(nn)
     n_nbrs <- nrow(nnt$dist)
     if (is.null(target)) {
@@ -113,12 +110,13 @@ smooth_knn_matrix <- function(nn,
   v <- affinity_matrix_res$matrix
   if (is_sparse_matrix(nn)) {
     # use j instead of i to transpose it
-    v <- Matrix::sparseMatrix(j = osparse$i, p = osparse$p, x = v,
-                              dims = osparse$dims, index1 = FALSE)
+    v <- Matrix::sparseMatrix(
+      j = osparse$i, p = osparse$p, x = v,
+      dims = osparse$dims, index1 = FALSE
+    )
     Matrix::diag(v) <- 0.0
     v <- Matrix::drop0(v)
-  }
-  else {
+  } else {
     v <- nng_to_sparse(nnt$idx, v, self_nbr = TRUE, by_row = FALSE)
   }
   affinity_matrix_res$matrix <- v
@@ -139,14 +137,16 @@ fuzzy_simplicial_set <- function(nn,
                                  n_threads = NULL,
                                  grain_size = 1,
                                  verbose = FALSE) {
-  affinity_matrix_res <- smooth_knn_matrix(nn = nn,
-                                           target = target,
-                                           local_connectivity = local_connectivity,
-                                           bandwidth = bandwidth,
-                                           ret_sigma = ret_sigma,
-                                           n_threads = n_threads,
-                                           grain_size = grain_size,
-                                           verbose = verbose)
+  affinity_matrix_res <- smooth_knn_matrix(
+    nn = nn,
+    target = target,
+    local_connectivity = local_connectivity,
+    bandwidth = bandwidth,
+    ret_sigma = ret_sigma,
+    n_threads = n_threads,
+    grain_size = grain_size,
+    verbose = verbose
+  )
   res <- fuzzy_set_union(affinity_matrix_res$matrix, set_op_mix_ratio = set_op_mix_ratio)
   if (ret_sigma) {
     res <- list(matrix = res)
@@ -214,8 +214,7 @@ perplexity_similarities <- function(nn, perplexity = NULL, ret_sigma = FALSE,
     if (!is.null(affinity_matrix_res$sigma)) {
       sigma <- affinity_matrix_res$sigma
     }
-  }
-  else {
+  } else {
     # knn kernel
     tsmessage("Using knn graph for input weights with k = ", ncol(nn$idx))
     # Make each row sum to 1, ignoring the self-index
@@ -248,14 +247,12 @@ nn_to_sparse <- function(nn_idxv, n_obs, val = 1, self_nbr = FALSE,
 
   if (length(val) == 1) {
     xs <- rep(val, n_obs * n_nbrs)
-  }
-  else {
+  } else {
     xs <- val
   }
   if (by_row) {
     is <- rep(1:n_obs, times = n_nbrs)
-  }
-  else {
+  } else {
     is <- rep(1:n_obs, each = n_nbrs)
   }
 
@@ -273,13 +270,14 @@ nng_to_sparse <- function(nn_idx, val = 1, self_nbr = FALSE,
                           max_nbr_id = NULL, by_row = TRUE) {
   if (by_row) {
     n_obs <- nrow(nn_idx)
-  }
-  else {
+  } else {
     n_obs <- ncol(nn_idx)
   }
 
-  nn_to_sparse(as.vector(nn_idx), n_obs, val = val, self_nbr = self_nbr,
-               max_nbr_id = max_nbr_id, by_row = by_row)
+  nn_to_sparse(as.vector(nn_idx), n_obs,
+    val = val, self_nbr = self_nbr,
+    max_nbr_id = max_nbr_id, by_row = by_row
+  )
 }
 
 # transpose the index and distance matrix

diff --git a/R/init.R b/R/init.R
@@ -19,8 +19,7 @@
 laplacian_eigenmap <- function(A, ndim = 2, verbose = FALSE, force_irlba = FALSE) {
   if (rspectra_is_installed() && !force_irlba) {
     coords <- rspectra_laplacian_eigenmap(A, ndim, verbose = verbose)
-  }
-  else {
+  } else {
     coords <- irlba_laplacian_eigenmap(A, ndim, verbose = verbose)
   }
   coords
@@ -96,8 +95,7 @@ form_modified_laplacian <- function(A, ret_d = FALSE) {
   Matrix::diag(L) <- 1 + Matrix::diag(L)
   if (ret_d) {
     list(L = L, Disqrt = 1 / Dsq)
-  }
-  else {
+  } else {
     L
   }
 }
@@ -111,8 +109,7 @@ sort_eigenvectors <- function(eig_res, ndim) {
 normalized_laplacian_init <- function(A, ndim = 2, verbose = FALSE, force_irlba = FALSE) {
   if (rspectra_is_installed() && !force_irlba) {
     coords <- rspectra_normalized_laplacian_init(A, ndim, verbose = verbose)
-  }
-  else {
+  } else {
     coords <- irlba_normalized_laplacian_init(A, ndim, verbose = verbose)
   }
   coords
@@ -166,22 +163,24 @@ irlba_spectral_tsvd <- function(L, n, iters = 1000) {
 }
 
 irlba_eigs_asym <- function(L, ndim) {
-  suppressWarnings(res <- tryCatch({
-    res <- irlba::partial_eigen(
-      L,
-      n = ndim + 1,
-      symmetric = FALSE,
-      smallest = TRUE,
-      tol = 1e-3,
-      maxit = 1000,
-      verbose = TRUE
-    )
-    res$values <- sqrt(res$values)
-    res
-  },
-  error = function(c) {
-    NULL
-  }))
+  suppressWarnings(res <- tryCatch(
+    {
+      res <- irlba::partial_eigen(
+        L,
+        n = ndim + 1,
+        symmetric = FALSE,
+        smallest = TRUE,
+        tol = 1e-3,
+        maxit = 1000,
+        verbose = TRUE
+      )
+      res$values <- sqrt(res$values)
+      res
+    },
+    error = function(c) {
+      NULL
+    }
+  ))
   res
 }
 
@@ -240,8 +239,7 @@ spectral_init <- function(A, ndim = 2, verbose = FALSE, force_irlba = FALSE) {
   if (rspectra_is_installed() && !force_irlba) {
     tsmessage("Initializing from normalized Laplacian + noise (using RSpectra)")
     coords <- rspectra_normalized_laplacian_init(A, ndim, verbose = FALSE)
-  }
-  else {
+  } else {
     tsmessage("Initializing from normalized Laplacian + noise (using irlba)")
     coords <- irlba_tsvd_normalized_laplacian_init(A, ndim, verbose = FALSE)
   }
@@ -262,7 +260,7 @@ irlba_spectral_init <- function(A, ndim = 2, verbose = FALSE) {
 # Scales coords so that the largest absolute coordinate is 10.0 then jitters by
 # adding gaussian noise with mean 0 and standard deviation sd
 scale_and_jitter <- function(coords, max_coord = 10.0, sd = 0.0001) {
-   expansion <- 10.0 / max(abs(coords))
+  expansion <- 10.0 / max(abs(coords))
   (coords * expansion) + matrix(stats::rnorm(n = prod(dim(coords)), sd = sd),
     ncol = ncol(coords)
   )
@@ -305,7 +303,7 @@ scale_coords <- function(X, sdev = 1e-4, verbose = FALSE) {
 # Returns the score matrix unless ret_extra is TRUE, in which case a list
 # is returned also containing the eigenvalues
 pca_init <- function(X, ndim = min(dim(X)), center = TRUE, ret_extra = FALSE,
-                       pca_method = "auto", verbose = FALSE) {
+                     pca_method = "auto", verbose = FALSE) {
   if (methods::is(X, "dist")) {
     res_mds <- stats::cmdscale(X, x.ret = TRUE, eig = TRUE, k = ndim)