Spelling fix (pytorch#108490)

a-r-r-o-w · pytorchmergebot · commit e08577aec542 · 2023-09-04T16:59:35.000Z
Fixes spelling mistake: non-deterinistic -> non-deterministic Pull Request resolved: pytorch#108490 Approved by: https://github.com/ezyang
diff --git a/torch/distributions/bernoulli.py b/torch/distributions/bernoulli.py
@@ -25,7 +25,7 @@ class Bernoulli(ExponentialFamily):
 
     Example::
 
-        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
         >>> m = Bernoulli(torch.tensor([0.3]))
         >>> m.sample()  # 30% chance 1; 70% chance 0
         tensor([ 0.])
diff --git a/torch/distributions/beta.py b/torch/distributions/beta.py
@@ -15,7 +15,7 @@ class Beta(ExponentialFamily):
 
     Example::
 
-        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
         >>> m = Beta(torch.tensor([0.5]), torch.tensor([0.5]))
         >>> m.sample()  # Beta distributed with concentration concentration1 and concentration0
         tensor([ 0.1046])
diff --git a/torch/distributions/binomial.py b/torch/distributions/binomial.py
@@ -24,7 +24,7 @@ class Binomial(Distribution):
 
     Example::
 
-        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
         >>> m = Binomial(100, torch.tensor([0 , .2, .8, 1]))
         >>> x = m.sample()
         tensor([   0.,   22.,   71.,  100.])
diff --git a/torch/distributions/categorical.py b/torch/distributions/categorical.py
@@ -36,7 +36,7 @@ class Categorical(Distribution):
 
     Example::
 
-        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
         >>> m = Categorical(torch.tensor([ 0.25, 0.25, 0.25, 0.25 ]))
         >>> m.sample()  # equal probability of 0, 1, 2, 3
         tensor(3)
diff --git a/torch/distributions/cauchy.py b/torch/distributions/cauchy.py
@@ -18,7 +18,7 @@ class Cauchy(Distribution):
 
     Example::
 
-        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
         >>> m = Cauchy(torch.tensor([0.0]), torch.tensor([1.0]))
         >>> m.sample()  # sample from a Cauchy distribution with loc=0 and scale=1
         tensor([ 2.3214])
diff --git a/torch/distributions/chi2.py b/torch/distributions/chi2.py
@@ -11,7 +11,7 @@ class Chi2(Gamma):
 
     Example::
 
-        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
         >>> m = Chi2(torch.tensor([1.0]))
         >>> m.sample()  # Chi2 distributed with shape df=1
         tensor([ 0.1046])
diff --git a/torch/distributions/continuous_bernoulli.py b/torch/distributions/continuous_bernoulli.py
@@ -29,7 +29,7 @@ class ContinuousBernoulli(ExponentialFamily):
 
     Example::
 
-        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
         >>> m = ContinuousBernoulli(torch.tensor([0.3]))
         >>> m.sample()
         tensor([ 0.2538])
diff --git a/torch/distributions/dirichlet.py b/torch/distributions/dirichlet.py
@@ -34,7 +34,7 @@ class Dirichlet(ExponentialFamily):
 
     Example::
 
-        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
         >>> m = Dirichlet(torch.tensor([0.5, 0.5]))
         >>> m.sample()  # Dirichlet distributed with concentration [0.5, 0.5]
         tensor([ 0.1046,  0.8954])
diff --git a/torch/distributions/exponential.py b/torch/distributions/exponential.py
@@ -14,7 +14,7 @@ class Exponential(ExponentialFamily):
 
     Example::
 
-        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
         >>> m = Exponential(torch.tensor([1.0]))
         >>> m.sample()  # Exponential distributed with rate=1
         tensor([ 0.1046])
diff --git a/torch/distributions/fishersnedecor.py b/torch/distributions/fishersnedecor.py
@@ -16,7 +16,7 @@ class FisherSnedecor(Distribution):
 
     Example::
 
-        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
         >>> m = FisherSnedecor(torch.tensor([1.0]), torch.tensor([2.0]))
         >>> m.sample()  # Fisher-Snedecor-distributed with df1=1 and df2=2
         tensor([ 0.2453])
diff --git a/torch/distributions/gamma.py b/torch/distributions/gamma.py
@@ -18,7 +18,7 @@ class Gamma(ExponentialFamily):
 
     Example::
 
-        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
         >>> m = Gamma(torch.tensor([1.0]), torch.tensor([1.0]))
         >>> m.sample()  # Gamma distributed with concentration=1 and rate=1
         tensor([ 0.1046])
diff --git a/torch/distributions/geometric.py b/torch/distributions/geometric.py
@@ -25,7 +25,7 @@ class Geometric(Distribution):
 
     Example::
 
-        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
         >>> m = Geometric(torch.tensor([0.3]))
         >>> m.sample()  # underlying Bernoulli has 30% chance 1; 70% chance 0
         tensor([ 2.])
diff --git a/torch/distributions/gumbel.py b/torch/distributions/gumbel.py
@@ -17,7 +17,7 @@ class Gumbel(TransformedDistribution):
 
     Examples::
 
-        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
         >>> m = Gumbel(torch.tensor([1.0]), torch.tensor([2.0]))
         >>> m.sample()  # sample from Gumbel distribution with loc=1, scale=2
         tensor([ 1.0124])
diff --git a/torch/distributions/half_cauchy.py b/torch/distributions/half_cauchy.py
@@ -19,7 +19,7 @@ class HalfCauchy(TransformedDistribution):
 
     Example::
 
-        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
         >>> m = HalfCauchy(torch.tensor([1.0]))
         >>> m.sample()  # half-cauchy distributed with scale=1
         tensor([ 2.3214])
diff --git a/torch/distributions/half_normal.py b/torch/distributions/half_normal.py
@@ -19,7 +19,7 @@ class HalfNormal(TransformedDistribution):
 
     Example::
 
-        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
         >>> m = HalfNormal(torch.tensor([1.0]))
         >>> m.sample()  # half-normal distributed with scale=1
         tensor([ 0.1046])
diff --git a/torch/distributions/kumaraswamy.py b/torch/distributions/kumaraswamy.py
@@ -24,7 +24,7 @@ class Kumaraswamy(TransformedDistribution):
 
     Example::
 
-        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
         >>> m = Kumaraswamy(torch.tensor([1.0]), torch.tensor([1.0]))
         >>> m.sample()  # sample from a Kumaraswamy distribution with concentration alpha=1 and beta=1
         tensor([ 0.1729])
diff --git a/torch/distributions/laplace.py b/torch/distributions/laplace.py
@@ -14,7 +14,7 @@ class Laplace(Distribution):
 
     Example::
 
-        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
         >>> m = Laplace(torch.tensor([0.0]), torch.tensor([1.0]))
         >>> m.sample()  # Laplace distributed with loc=0, scale=1
         tensor([ 0.1046])
diff --git a/torch/distributions/lkj_cholesky.py b/torch/distributions/lkj_cholesky.py
@@ -37,7 +37,7 @@ class LKJCholesky(Distribution):
 
     Example::
 
-        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
         >>> l = LKJCholesky(3, 0.5)
         >>> l.sample()  # l @ l.T is a sample of a correlation 3x3 matrix
         tensor([[ 1.0000,  0.0000,  0.0000],
diff --git a/torch/distributions/log_normal.py b/torch/distributions/log_normal.py
@@ -16,7 +16,7 @@ class LogNormal(TransformedDistribution):
 
     Example::
 
-        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
         >>> m = LogNormal(torch.tensor([0.0]), torch.tensor([1.0]))
         >>> m.sample()  # log-normal distributed with mean=0 and stddev=1
         tensor([ 0.1046])
diff --git a/torch/distributions/logistic_normal.py b/torch/distributions/logistic_normal.py
@@ -23,7 +23,7 @@ class LogisticNormal(TransformedDistribution):
 
         >>> # logistic-normal distributed with mean=(0, 0, 0) and stddev=(1, 1, 1)
         >>> # of the base Normal distribution
-        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
         >>> m = LogisticNormal(torch.tensor([0.0] * 3), torch.tensor([1.0] * 3))
         >>> m.sample()
         tensor([ 0.7653,  0.0341,  0.0579,  0.1427])
diff --git a/torch/distributions/normal.py b/torch/distributions/normal.py
@@ -16,7 +16,7 @@ class Normal(ExponentialFamily):
 
     Example::
 
-        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
         >>> m = Normal(torch.tensor([0.0]), torch.tensor([1.0]))
         >>> m.sample()  # normally distributed with loc=0 and scale=1
         tensor([ 0.1046])
diff --git a/torch/distributions/one_hot_categorical.py b/torch/distributions/one_hot_categorical.py
@@ -26,7 +26,7 @@ class OneHotCategorical(Distribution):
 
     Example::
 
-        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
         >>> m = OneHotCategorical(torch.tensor([ 0.25, 0.25, 0.25, 0.25 ]))
         >>> m.sample()  # equal probability of 0, 1, 2, 3
         tensor([ 0.,  0.,  0.,  1.])
diff --git a/torch/distributions/pareto.py b/torch/distributions/pareto.py
@@ -13,7 +13,7 @@ class Pareto(TransformedDistribution):
 
     Example::
 
-        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
         >>> m = Pareto(torch.tensor([1.0]), torch.tensor([1.0]))
         >>> m.sample()  # sample from a Pareto distribution with scale=1 and alpha=1
         tensor([ 1.5623])
diff --git a/torch/distributions/relaxed_bernoulli.py b/torch/distributions/relaxed_bernoulli.py
@@ -113,7 +113,7 @@ class RelaxedBernoulli(TransformedDistribution):
 
     Example::
 
-        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
         >>> m = RelaxedBernoulli(torch.tensor([2.2]),
         ...                      torch.tensor([0.1, 0.2, 0.3, 0.99]))
         >>> m.sample()
diff --git a/torch/distributions/relaxed_categorical.py b/torch/distributions/relaxed_categorical.py
@@ -101,7 +101,7 @@ class RelaxedOneHotCategorical(TransformedDistribution):
 
     Example::
 
-        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
         >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]),
         ...                              torch.tensor([0.1, 0.2, 0.3, 0.4]))
         >>> m.sample()
diff --git a/torch/distributions/studentT.py b/torch/distributions/studentT.py
@@ -16,7 +16,7 @@ class StudentT(Distribution):
 
     Example::
 
-        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
         >>> m = StudentT(torch.tensor([2.0]))
         >>> m.sample()  # Student's t-distributed with degrees of freedom=2
         tensor([ 0.1046])
diff --git a/torch/distributions/von_mises.py b/torch/distributions/von_mises.py
@@ -110,7 +110,7 @@ class VonMises(Distribution):
     interpreted as angles modulo 2 pi.
 
     Example::
-        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
         >>> m = VonMises(torch.tensor([1.0]), torch.tensor([1.0]))
         >>> m.sample()  # von Mises distributed with loc=1 and concentration=1
         tensor([1.9777])
diff --git a/torch/distributions/weibull.py b/torch/distributions/weibull.py
@@ -15,7 +15,7 @@ class Weibull(TransformedDistribution):
 
     Example:
 
-        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
         >>> m = Weibull(torch.tensor([1.0]), torch.tensor([1.0]))
         >>> m.sample()  # sample from a Weibull distribution with scale=1, concentration=1
         tensor([ 0.4784])
