Cleaned up dimensionality scaling notebooks

configs/conf_dims.yaml

@@ -1,12 +1,22 @@
 exp_log_name: "scaling_dims" # optional but recommended
+
+# datasets to use
 data: ["multivariate_normal"]
+
+# number of samples and dimensions
+n: [5000]
+d: [100]
+
+# MMD bandwidth parameter
+mmd_bandwidth: [10., 20., 20.] 
+
+# dimensionality scaling experiments
 experiments: ["ScaleDimSW", "ScaleDimC2ST", "ScaleDimMMD"]
-n: [10000]
-d: [1000]
-distort: ["shift_one", "shift_all", "increase_var"]
 dim_sizes: [5, 10, 50, 100, 500, 1000]
-mmd_bandwidth: [10., 10., 10.]
+runs: 5 # number of sample selection for errorbars
+distort: ["shift_one", "shift_all", "increase_var"]
 
+# seed for reproducibility
 seed: 1404
-runs: 5
+
 

labproject/data.py

@@ -275,15 +275,15 @@ def multivariate_normal(n=3000, dims=100, means=None, vars=None, distort=None):
         ), "The length of the means vector must be equal to the number of dimensions"
     if vars is None:
         if distort == "increase_var":
-            vars = torch.eye(dims) * 1.1
+            vars = torch.eye(dims) * 2
         else:
             vars = torch.eye(dims)
     else:
         assert (
             len(vars) == dims
         ), "The length of the vars vector must be equal to the number of dimensions"
         if distort == "increase_var":
-            vars = torch.diag(vars) * 1.1
+            vars = torch.diag(vars) * 2
         else:
             vars = torch.diag(vars)