Bump Nx to 0.13

.github/workflows/ci.yml

@@ -14,8 +14,8 @@ jobs:
           - elixir: "1.19.4"
             otp: "27.3"
             lint: true
-          - elixir: "1.15.8"
-            otp: "26.1"
+          - elixir: "1.17.3"
+            otp: "26.2"
     steps:
       - uses: actions/checkout@v2
 

lib/scholar/covariance/ledoit_wolf.ex

@@ -54,19 +54,19 @@ defmodule Scholar.Covariance.LedoitWolf do
       #Nx.Tensor<
         f32[2][2]
         [
-          [0.3557686507701874, 0.17340737581253052],
-          [0.17340737581253052, 1.0300586223602295]
+          [0.35576865, 0.17340738],
+          [0.17340738, 1.0300586]
         ]
       >
       iex> model.shrinkage
       #Nx.Tensor<
         f32
-        0.15034137666225433
+        0.15034138
       >
       iex> model.location
       #Nx.Tensor<
         f32[2]
-        [0.17184630036354065, 0.3276958167552948]
+        [0.1718463, 0.32769582]
       >
 
       iex> key = Nx.Random.key(0)
@@ -76,20 +76,20 @@ defmodule Scholar.Covariance.LedoitWolf do
       #Nx.Tensor<
         f32[3][3]
         [
-          [2.5945029258728027, 1.5078359842300415, 1.1623677015304565],
-          [1.5078359842300415, 2.106797456741333, 1.1812156438827515],
-          [1.1623677015304565, 1.1812156438827515, 1.4606266021728516]
+          [2.594503, 1.507836, 1.1623677],
+          [1.507836, 2.1067975, 1.1812156],
+          [1.1623677, 1.1812156, 1.4606266]
         ]
       >
       iex> model.shrinkage
       #Nx.Tensor<
         f32
-        0.1908363401889801
+        0.19083634
       >
       iex> model.location
       #Nx.Tensor<
         f32[3]
-        [1.1228725910186768, 0.5419300198554993, 0.8678852319717407]
+        [1.1228726, 0.54193, 0.86788523]
       >
 
       iex> key = Nx.Random.key(0)
@@ -99,9 +99,9 @@ defmodule Scholar.Covariance.LedoitWolf do
       #Nx.Tensor<
         f32[3][3]
         [
-          [3.8574986457824707, 2.2048025131225586, 2.1504499912261963],
-          [2.2048025131225586, 2.4572863578796387, 1.7215262651443481],
-          [2.1504499912261963, 1.7215262651443481, 2.154898166656494]
+          [3.8574986, 2.2048025, 2.15045],
+          [2.2048025, 2.4572864, 1.7215263],
+          [2.15045, 1.7215263, 2.1548982]
         ]
       >
   """

lib/scholar/covariance/shrunk_covariance.ex

@@ -51,14 +51,14 @@ defmodule Scholar.Covariance.ShrunkCovariance do
       #Nx.Tensor<
         f32[2][2]
         [
-          [0.7721845507621765, 0.19141492247581482],
-          [0.19141492247581482, 0.33952537178993225]
+          [0.77218455, 0.19141492],
+          [0.19141492, 0.33952537]
         ]
       >
       iex> model.location
       #Nx.Tensor<
         f32[2]
-        [0.18202415108680725, -0.09216632694005966]
+        [0.18202415, -0.09216633]
       >
       iex> key = Nx.Random.key(0)
       iex> {x, _new_key} = Nx.Random.multivariate_normal(key, Nx.tensor([0.0, 0.0]), Nx.tensor([[0.8, 0.3], [0.2, 0.4]]), shape: {10}, type: :f32)
@@ -67,14 +67,14 @@ defmodule Scholar.Covariance.ShrunkCovariance do
       #Nx.Tensor<
         f32[2][2]
         [
-          [0.7000747323036194, 0.1276099532842636],
-          [0.1276099532842636, 0.41163527965545654]
+          [0.70007473, 0.12760995],
+          [0.12760995, 0.41163528]
         ]
       >
       iex> model.location
       #Nx.Tensor<
         f32[2]
-        [0.18202415108680725, -0.09216632694005966]
+        [0.18202415, -0.09216633]
       >
   """
 

lib/scholar/cross_decomposition/pls_svd.ex

@@ -90,15 +90,15 @@ defmodule Scholar.CrossDecomposition.PLSSVD do
       iex> model.y_std
       #Nx.Tensor<
         f32[2]
-        [5.467098712921143, 5.661198616027832]
+        [5.4670987, 5.6611986]
       >
       iex> model.x_weights
       #Nx.Tensor<
         f32[3][2]
         [
-          [0.521888256072998, -0.11256571859121323],
-          [0.6170258522033691, 0.7342619299888611],
-          [0.5889922380447388, -0.6694686412811279]
+          [0.5218878, -0.11256802],
+          [0.6170291, 0.7342591],
+          [0.58898926, -0.66947126]
         ]
       >
   """
@@ -115,7 +115,15 @@ defmodule Scholar.CrossDecomposition.PLSSVD do
 
     c = Nx.dot(x, [0], y, [0])
 
-    {u, _s, vt} = Nx.LinAlg.svd(c, full_matrices?: false)
+    # full_matrices?: true on purpose: the false path forms the Gram matrix
+    # CᵀC to avoid the larger QR/Halley iteration, which squares the
+    # condition number and can cost the smaller singular vectors real
+    # precision (Nx.LinAlg.svd's own docs call this out). C here is the
+    # cross-covariance of two already small, already centered and scaled
+    # blocks, so its singular values are routinely far apart, exactly the
+    # regime that trade-off hurts. true costs more but keeps x_weights and
+    # y_weights properly orthonormal.
+    {u, _s, vt} = Nx.LinAlg.svd(c, full_matrices?: true)
     u = Nx.slice_along_axis(u, 0, num_components, axis: 1)
     vt = Nx.slice_along_axis(vt, 0, num_components, axis: 0)
     {u, vt} = Scholar.Decomposition.Utils.flip_svd(u, vt)
@@ -171,20 +179,20 @@ defmodule Scholar.CrossDecomposition.PLSSVD do
       #Nx.Tensor<
         f32[4][2]
         [
-          [-1.397004246711731, -0.10283949971199036],
-          [-1.1967883110046387, 0.17159013450145721],
-          [0.5603229403495789, -0.10849219560623169],
-          [2.0334696769714355, 0.039741579443216324]
+          [-1.3970047, -0.10283327],
+          [-1.1967875, 0.17159548],
+          [0.56032246, -0.10849468],
+          [2.0334697, 0.039732467]
         ]
       >
       iex> y
       #Nx.Tensor<
         f32[4][2]
         [
-          [-1.2260178327560425, -0.019306711852550507],
-          [-0.9602956175804138, 0.04015407711267471],
-          [0.3249155580997467, -0.04311027377843857],
-          [1.8613981008529663, 0.022262824699282646]
+          [-1.226018, -0.019301286],
+          [-0.9602954, 0.040158324],
+          [0.32491535, -0.043111708],
+          [1.8613981, 0.022254586]
         ]
       >
 
@@ -252,20 +260,20 @@ defmodule Scholar.CrossDecomposition.PLSSVD do
       #Nx.Tensor<
         f32[4][2]
         [
-          [-1.397004246711731, -0.10283949971199036],
-          [-1.1967883110046387, 0.17159013450145721],
-          [0.5603229403495789, -0.10849219560623169],
-          [2.0334696769714355, 0.039741579443216324]
+          [-1.3970047, -0.10283327],
+          [-1.1967875, 0.17159548],
+          [0.56032246, -0.10849468],
+          [2.0334697, 0.039732467]
         ]
       >
       iex> y
       #Nx.Tensor<
         f32[4][2]
         [
-          [-1.2260178327560425, -0.019306711852550507],
-          [-0.9602956175804138, 0.04015407711267471],
-          [0.3249155580997467, -0.04311027377843857],
-          [1.8613981008529663, 0.022262824699282646]
+          [-1.226018, -0.019301286],
+          [-0.9602954, 0.040158324],
+          [0.32491535, -0.043111708],
+          [1.8613981, 0.022254586]
         ]
       >
 

lib/scholar/decomposition/kernel_pca.ex

@@ -95,7 +95,7 @@ defmodule Scholar.Decomposition.KernelPCA do
       iex> x = Nx.tensor([[0.5, 0.2, 0.8], [1.0, 0.5, 0.2], [0.3, 1.0, 0.7], [0.9, 0.1, 1.0]])
       iex> kpca = Scholar.Decomposition.KernelPCA.fit(x, num_components: 2, kernel: :rbf)
       iex> kpca.eigenvalues
-      Nx.tensor([0.3644833564758301, 0.2607457637786865])
+      Nx.tensor([0.3644834, 0.26074564])
   """
   deftransform fit(x, opts \\ []) do
     opts = NimbleOptions.validate!(opts, @opts_schema)
@@ -164,7 +164,7 @@ defmodule Scholar.Decomposition.KernelPCA do
       iex> Scholar.Decomposition.KernelPCA.transform(kpca, Nx.tensor([[0.5, 0.5, 0.5]]))
       Nx.tensor(
         [
-          [0.12500187754631042, 0.02909737080335617]
+          [0.12497942, 0.029221743]
         ]
       )
   """
@@ -216,10 +216,10 @@ defmodule Scholar.Decomposition.KernelPCA do
       iex> Scholar.Decomposition.KernelPCA.fit_transform(x, num_components: 2, kernel: :rbf)
       Nx.tensor(
         [
-          [-0.13561572134494781, -0.16519643366336823],
-          [0.021601030603051186, 0.44114425778388977],
-          [0.4687873423099518, -0.1576462835073471],
-          [-0.3547726571559906, -0.1183016300201416]
+          [-0.13543777, -0.16535977],
+          [0.021126157, 0.44115686],
+          [0.4689568, -0.15726776],
+          [-0.3546452, -0.1185294]
         ]
       )
   """

lib/scholar/decomposition/pca.ex

@@ -100,12 +100,12 @@ defmodule Scholar.Decomposition.PCA do
       iex> pca.components
       Nx.tensor(
         [
-          [0.36182016134262085, -0.08202514797449112, 0.8565111756324768, 0.3588128685951233],
-          [0.6585038900375366, 0.7275884747505188, -0.17632202804088593, -0.07679986208677292]
+          [0.36158934, -0.082268484, 0.8565728, 0.35884288],
+          [0.65654594, 0.72970587, -0.17576799, -0.074712664]
         ]
       )
       iex> pca.singular_values
-      Nx.tensor([25.089859008789062, 6.007821559906006])
+      Nx.tensor([25.089865, 6.007852])
   """
   deftransform fit(x, opts \\ []) do
     opts = NimbleOptions.validate!(opts, @opts_schema)
@@ -210,12 +210,12 @@ defmodule Scholar.Decomposition.PCA do
       iex> pca.components
       Nx.tensor(
         [
-          [-0.33354005217552185, 0.1048964187502861, -0.8618107080105579, -0.3674643635749817],
-          [-0.5862125754356384, -0.7916879057884216, 0.15874788165092468, -0.06621300429105759]
+          [-0.3331483, 0.105326004, -0.86190677, -0.36747244],
+          [-0.58288467, -0.79399943, 0.15797591, -0.06967129]
         ]
       )
       iex> pca.singular_values
-      Nx.tensor([77.05782028025969, 10.137848854064941])
+      Nx.tensor([77.05691, 10.158366])
   """
   deftransform incremental_fit(batches, opts) do
     opts = NimbleOptions.validate!(opts, @opts_schema)
@@ -242,12 +242,12 @@ defmodule Scholar.Decomposition.PCA do
     iex> pca.components
     Nx.tensor(
       [
-        [-0.3229745328426361, 0.09587063640356064, -0.8628664612770081, -0.37677285075187683],
-        [-0.6786625981330872, -0.7167785167694092, 0.14237160980701447, 0.07332050055265427]
+        [-0.32289746, 0.095940694, -0.8628819, -0.37678596],
+        [-0.67544055, -0.7201917, 0.141901, 0.070487075]
       ]
     )
     iex> pca.singular_values
-    Nx.tensor([166.141845703125, 6.078948020935059])
+    Nx.tensor([166.14182, 6.078579])
   """
   deftransform partial_fit(model, x) do
     if Nx.rank(x) != 2 do
@@ -373,9 +373,9 @@ defmodule Scholar.Decomposition.PCA do
       iex> Scholar.Decomposition.PCA.transform(pca, x_transform)
       Nx.tensor(
         [
-          [-1.4739344120025635, -0.48932668566703796],
-          [0.28113049268722534, 0.2337251454591751],
-          [2.0361955165863037, 0.9567767977714539]
+          [-1.4737695, -0.49078038],
+          [0.28105056, 0.23384926],
+          [2.0358706, 0.9584787]
         ]
       )
   """
@@ -442,13 +442,12 @@ defmodule Scholar.Decomposition.PCA do
       iex> Scholar.Decomposition.PCA.fit_transform(x, num_components: 2)
       Nx.tensor(
         [
-          [0.16441848874092102, 0.028548287227749825],
-          [-0.32804328203201294, 0.20709986984729767],
-          [-0.3284338414669037, -0.08318747580051422],
-          [-0.42237386107444763, -0.0735677033662796],
-          [0.17480169236660004, -0.11189625412225723],
-          [0.7396301627159119, 0.03300142288208008
-          ]
+          [0.16341472, -0.017230444],
+          [-0.33249766, 0.1893504],
+          [-0.32686582, -0.10110473],
+          [-0.42023695, -0.005525116],
+          [0.1768687, -0.14014886],
+          [0.7393165, 0.074657336]
         ]
       )
   """

lib/scholar/decomposition/truncated_svd.ex

@@ -81,14 +81,14 @@ defmodule Scholar.Decomposition.TruncatedSVD do
       #Nx.Tensor<
         f32[2][2]
         [
-          [0.6871105432510376, 0.7265529036521912],
-          [0.7265529036521912, -0.6871105432510376]
+          [0.68631727, 0.72730225],
+          [0.72730225, -0.68631727]
         ]
       >
       iex> tsvd.singular_values
       #Nx.Tensor<
         f32[2]
-        [7.528080940246582, 0.7601959705352783]
+        [7.5280886, 0.7601881]
       >
 
   """
@@ -119,10 +119,10 @@ defmodule Scholar.Decomposition.TruncatedSVD do
         f32[5][2]
         [
           [0.0, 0.0],
-          [0.6871105432510376, 0.7265529036521912],
-          [1.413663387298584, 0.039442360401153564],
-          [4.240990161895752, 0.1183270812034607],
-          [6.017930030822754, -0.18578583002090454]
+          [0.68631727, 0.72730225],
+          [1.4136195, 0.04098499],
+          [4.2408586, 0.122954965],
+          [6.0181293, -0.17921868]
         ]
       >
       iex> key = Nx.Random.key(0)
@@ -131,11 +131,11 @@ defmodule Scholar.Decomposition.TruncatedSVD do
       #Nx.Tensor<
         f32[5][2]
         [
-          [1.9478826522827148, 2.260593891143799],
-          [2.481153964996338, 1.906071662902832],
-          [3.023407220840454, 1.352442979812622],
-          [5.174456596374512, -0.46385863423347473],
-          [6.521108150482178, -1.6488237380981445]
+          [1.9499695, 2.258082],
+          [2.4829183, 1.9048187],
+          [3.024655, 1.3492495],
+          [5.1740265, -0.46841517],
+          [6.5195804, -1.655032]
         ]
       >
   """

lib/scholar/interpolation/bezier_spline.ex

@@ -43,13 +43,13 @@ defmodule Scholar.Interpolation.BezierSpline do
           [
             [
               [0.0, 2.0],
-              [0.3333331048488617, 1.0333333015441895],
-              [0.6666665077209473, 0.06666667759418488],
+              [0.33333334, 1.0333333],
+              [0.66666675, 0.06666667],
               [1.0, 0.0]
             ],
             [
               [1.0, 0.0],
-              [1.3333334922790527, -0.06666667759418488],
+              [1.3333333, -0.06666667],
               [1.6666665077209473, 0.7666666507720947],
               [2.0, 1.0]
             ],