update requirements and numpy math api

AaltoML · Dec 14, 2023 · b899eed · b899eed
1 parent e9d6970
commit b899eed
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Showing 13 changed files with 18 additions and 17 deletions.
diff --git a/demos/classification.py b/demos/classification.py
@@ -14,11 +14,11 @@
 x = np.concatenate([x0, np.array([50]), x1], axis=0)
 # x = np.linspace(np.min(x), np.max(x), N)
 f = lambda x_: 6 * np.sin(np.pi * x_ / 10.0) / (np.pi * x_ / 10.0 + 1)
-y_ = f(x) + np.math.sqrt(0.05)*np.random.randn(x.shape[0])
+y_ = f(x) + np.sqrt(0.05)*np.random.randn(x.shape[0])
 y = np.sign(y_)
 y[y == -1] = 0
 x_test = np.linspace(np.min(x)-5.0, np.max(x)+5.0, num=500)
-y_test = np.sign(f(x_test) + np.math.sqrt(0.05)*np.random.randn(x_test.shape[0]))
+y_test = np.sign(f(x_test) + np.sqrt(0.05)*np.random.randn(x_test.shape[0]))
 y_test[y_test == -1] = 0
 x_plot = np.linspace(np.min(x)-10.0, np.max(x)+10.0, num=500)
 z = np.linspace(min(x), max(x), num=M)

diff --git a/demos/positive.py b/demos/positive.py
@@ -19,9 +19,9 @@ def wiggly_time_series(x_):
 # x = np.linspace(np.min(x), np.max(x), N)
 # f = lambda x_: 3 * np.sin(np.pi * x_ / 10.0)
 f = wiggly_time_series
-y = nonlinearity(f(x)) + np.math.sqrt(0.1)*np.random.randn(x.shape[0])
+y = nonlinearity(f(x)) + np.sqrt(0.1)*np.random.randn(x.shape[0])
 x_test = np.linspace(np.min(x), np.max(x), num=500)
-y_test = nonlinearity(f(x_test)) + np.math.sqrt(0.05)*np.random.randn(x_test.shape[0])
+y_test = nonlinearity(f(x_test)) + np.sqrt(0.05)*np.random.randn(x_test.shape[0])
 x_plot = np.linspace(np.min(x)-10.0, np.max(x)+10.0, num=500)
 
 M = 20

diff --git a/demos/regression.py b/demos/regression.py
@@ -9,8 +9,8 @@ def wiggly_time_series(x_):
     noise_var = 0.2  # true observation noise
     # return 0.25 * (np.cos(0.04*x_+0.33*np.pi) * np.sin(0.2*x_) +
     return (np.cos(0.04*x_+0.33*np.pi) * np.sin(0.2*x_) +
-            np.math.sqrt(noise_var) * np.random.normal(0, 1, x_.shape) +
-            # np.math.sqrt(noise_var) * np.random.uniform(-4, 4, x_.shape) +
+            np.sqrt(noise_var) * np.random.normal(0, 1, x_.shape) +
+            # np.sqrt(noise_var) * np.random.uniform(-4, 4, x_.shape) +
             0.0 * x_)  # 0.02 * x_)
             # 0.0 * x_) + 2.5  # 0.02 * x_)
 

diff --git a/demos/studentt.py b/demos/studentt.py
@@ -9,7 +9,7 @@
 def wiggly_time_series(x_):
     noise_var = 0.2  # true observation noise scale
     return (np.cos(0.04*x_+0.33*np.pi) * np.sin(0.2*x_) +
-            np.math.sqrt(noise_var) * np.random.standard_t(3., x_.shape) +
+            np.sqrt(noise_var) * np.random.standard_t(3., x_.shape) +
             0.0 * x_)
 
 

diff --git a/experiments/binary/binary.py b/experiments/binary/binary.py
@@ -11,7 +11,7 @@
 x = np.sort(70 * np.random.rand(N))
 sn = 0.01
 f = lambda x_: 12. * np.sin(4 * np.pi * x_) / (0.25 * np.pi * x_ + 1)
-y_ = f(x) + np.math.sqrt(sn)*np.random.randn(x.shape[0])
+y_ = f(x) + np.sqrt(sn)*np.random.randn(x.shape[0])
 y = np.sign(y_)
 y[y == -1] = 0
 

diff --git a/requirements.txt b/requirements.txt
@@ -5,5 +5,6 @@ numba
 numpy
 matplotlib
 scipy
-sklearn
-pandas
+scikit-learn
+pandas
+tensorflow_probability
diff --git a/tests/normaliser_test.py b/tests/normaliser_test.py
@@ -13,7 +13,7 @@
 def wiggly_time_series(x_):
     noise_var = 0.15  # true observation noise
     return (np.cos(0.04*x_+0.33*np.pi) * np.sin(0.2*x_) +
-            np.math.sqrt(noise_var) * np.random.normal(0, 1, x_.shape) +
+            np.sqrt(noise_var) * np.random.normal(0, 1, x_.shape) +
             0.0 * x_)  # 0.02 * x_)
 
 

diff --git a/tests/test_gp_vs_markovgp_class.py b/tests/test_gp_vs_markovgp_class.py
@@ -11,7 +11,7 @@ def build_data(N):
     x = 100 * np.random.rand(N)
     x = np.sort(x)  # since MarkovGP sorts the inputs, they must also be sorted for GP
     f = lambda x_: 6 * np.sin(np.pi * x_ / 10.0) / (np.pi * x_ / 10.0 + 1)
-    y_ = f(x) + np.math.sqrt(0.05) * np.random.randn(x.shape[0])
+    y_ = f(x) + np.sqrt(0.05) * np.random.randn(x.shape[0])
     y = np.sign(y_)
     y[y == -1] = 0
     x = x[:, None]

diff --git a/tests/test_gp_vs_markovgp_reg.py b/tests/test_gp_vs_markovgp_reg.py
@@ -9,7 +9,7 @@
 def wiggly_time_series(x_):
     noise_var = 0.15  # true observation noise
     return (np.cos(0.04*x_+0.33*np.pi) * np.sin(0.2*x_) +
-            np.math.sqrt(noise_var) * np.random.normal(0, 1, x_.shape))
+            np.sqrt(noise_var) * np.random.normal(0, 1, x_.shape))
 
 
 def build_data(N):

diff --git a/tests/test_sparsemarkov.py b/tests/test_sparsemarkov.py
@@ -12,7 +12,7 @@
 def wiggly_time_series(x_):
     noise_var = 0.15  # true observation noise
     return (np.cos(0.04*x_+0.33*np.pi) * np.sin(0.2*x_) +
-            np.math.sqrt(noise_var) * np.random.normal(0, 1, x_.shape))
+            np.sqrt(noise_var) * np.random.normal(0, 1, x_.shape))
 
 
 def build_data(N):

diff --git a/tests/test_vs_exact_marg_lik.py b/tests/test_vs_exact_marg_lik.py
@@ -9,7 +9,7 @@
 def wiggly_time_series(x_):
     noise_var = 0.15  # true observation noise
     return (np.cos(0.04*x_+0.33*np.pi) * np.sin(0.2*x_) +
-            np.math.sqrt(noise_var) * np.random.normal(0, 1, x_.shape))
+            np.sqrt(noise_var) * np.random.normal(0, 1, x_.shape))
 
 
 def build_data(N):

diff --git a/tests/test_vs_gpflow_class.py b/tests/test_vs_gpflow_class.py
@@ -15,7 +15,7 @@ def build_data(N):
     # np.random.seed(12345)
     x = 100 * np.random.rand(N)
     f = lambda x_: 6 * np.sin(np.pi * x_ / 10.0) / (np.pi * x_ / 10.0 + 1)
-    y_ = f(x) + np.math.sqrt(0.05) * np.random.randn(x.shape[0])
+    y_ = f(x) + np.sqrt(0.05) * np.random.randn(x.shape[0])
     y = np.sign(y_)
     y[y == -1] = 0
     x = x[:, None]

diff --git a/tests/test_vs_gpflow_reg.py b/tests/test_vs_gpflow_reg.py
@@ -12,7 +12,7 @@
 def wiggly_time_series(x_):
     noise_var = 0.15  # true observation noise
     return (np.cos(0.04*x_+0.33*np.pi) * np.sin(0.2*x_) +
-            np.math.sqrt(noise_var) * np.random.normal(0, 1, x_.shape))
+            np.sqrt(noise_var) * np.random.normal(0, 1, x_.shape))
 
 
 def build_data(N):