more CI/CD

.github/workflows/python-package.yml

@@ -23,12 +23,16 @@ jobs:
         python-version: ${{ matrix.python-version }}
     - name: Install numpy
       run: |
-        pip install numpy
+        pip install numpy coverage flake8
     - name: Test unittests
       run: |
-        python -m unittest discover -s ./test  -p 'test_*.py'
-#    - name: Lint with flake8
-#      run: |
+        python -m coverage run -m unittest discover -s ./test  -p 'test_*.py'
+    - name: Coverage
+      run: |
+        coverage report --include=ctrl_c_nn.py
+    - name: Lint with flake8
+      run: |
+        flake8 ctrl_c_nn.py --ignore=E202,E501,F401,E226 --count --show-source --statistics
 #        # stop the build if there are Python syntax errors or undefined names
 #        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
 #        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide

README.md

@@ -38,6 +38,7 @@ g = e.permute((3,0,2,1)) # shape (1, 1, 4, 2)
 ```
 
 ## Sample Usage NN
+```python
 from ctrl_c_nn import nn, Tensor
 
 model = nn.Sequential(
@@ -60,9 +61,9 @@ for i in range(2000):
     output_tensor = model(input_tensor)
     loss = loss_fn(output_tensor, target_tensor)
 
-    print("loss", loss.item(), "          iteration", i)
+    print("loss", loss.item(), " iteration", i)
 
     dout = loss_fn.backward(loss)
     dout = model.backward(dout)
     model.update(lr=0.001)
-
+```

ctrl_c_nn.py

@@ -7,6 +7,7 @@
 
 sumprod = math.sumprod if sys.version_info >= (3, 12) else lambda p, q: sum([p_i*q_i for p_i, q_i in zip(p, q)])
 
+
 class LLOps:
     """
     Class for (recursive) functional operations on lists of lists
@@ -662,7 +663,7 @@ def forward(self, x: Tensor):
             result = Tensor.zeros((B, C_out, H_out, W_out))
             for u in range(self.padding, H - self.padding, self.stride):
                 for v in range(self.padding, H - self.padding, self.stride):
-                    x_chunk = x[:, :, u:u+K, v:v+K].reshape((B,C_in, K*K))  # reshaped from B, C_in, K, K
+                    x_chunk = x[:, :, u:u+K, v:v+K].reshape((B, C_in, K*K))  # reshaped from B, C_in, K, K
                     result[:, :, u, v] = x_chunk @ self.w + self.b
                     #                   (B,C_in, K,K) @( C_out, C_in, K, K) + ( C_out)
                     # TODO complete

test/test_tensor.py

@@ -1,5 +1,8 @@
 import unittest
 import numpy as np
+
+from ctrl_c_nn import Tensor
+
 try:
     import torch
 except ImportError:
@@ -148,6 +151,16 @@ def test_basic_mul(self):
         result_B = ccm.Tensor(mat1.tolist()) * ccm.Tensor(mat2.tolist())
         self.assertEqual(result_A.tolist(), result_B.tolist(),f"Tensor operation basic mul failed.")
 
+    def test_mul_dimless_tensor(self):
+        mat1 = np.array([3, 2])
+        mat2 = np.array(2)
+        result_A = mat1 * mat2
+        result_B = ccm.Tensor(mat1.tolist()) * ccm.Tensor(mat2.tolist())
+        self.assertEqual(result_A.tolist(), result_B.tolist(), f"Tensor operation dimless mul failed.")
+        result_C = mat2 * mat1
+        result_D = ccm.Tensor(mat2.tolist()) * ccm.Tensor(mat1.tolist())
+        self.assertEqual(result_C.tolist(), result_D.tolist(), f"Tensor operation dimless mul failed.")
+
     def test_basic_matmul(self):
         mat1 = np.random.randint(0, 10, size=(400, 200))
         mat2 = np.random.randint(0, 10, size=(200, 300))
@@ -319,3 +332,42 @@ def test_permute(self):
         tensor_ctrlc = ccm.Tensor(tensor_np.tolist())
         self.assertEqual(np.transpose(tensor_np, (4, 1, 0, 3, 5, 2)).tolist(), tensor_ctrlc.permute((4, 1, 0, 3, 5, 2)).tolist(), f"Tensor permute 6d does not work.")
 
+
+class TestTensorCreation(unittest.TestCase):
+
+    def test_create(self):
+        l1 = [[2, 1, 3], [4, 2, 1]]
+        self.assertEqual(l1, Tensor(l1).tolist(), f"Tensor create and to_list is not identical.")
+        shape = (3, 2, 1)
+        t1 = Tensor.zeros(shape)
+        t2 = Tensor.ones(shape)
+        t3 = Tensor.random_float(shape)
+        t4 = Tensor.random_int(shape)
+        t5 = Tensor.random_float(shape, min=-2, max=+2)
+        t6 = Tensor.random_int(shape, min=-2, max=+2)
+        t7 = Tensor.fill(shape, 27)
+        self.assertEqual(shape, t1.shape, f"Tensor create gives wrong shape.")
+        self.assertEqual(shape, t2.shape, f"Tensor create gives wrong shape.")
+        self.assertEqual(shape, t3.shape, f"Tensor create gives wrong shape.")
+        self.assertEqual(shape, t4.shape, f"Tensor create gives wrong shape.")
+        self.assertEqual(shape, t5.shape, f"Tensor create gives wrong shape.")
+        self.assertEqual(shape, t6.shape, f"Tensor create gives wrong shape.")
+        self.assertEqual(shape, t7.shape, f"Tensor create gives wrong shape.")
+
+    def test_create_single_int(self):
+        n1 = np.array(4)
+        t1 = Tensor(4)
+        self.assertEqual(n1.shape, t1.shape, f"Wrong shape when create tensor from int.")
+        self.assertEqual(n1.ndim, t1.ndim, f"Wrong ndim when create tensor from int.")
+
+        self.assertEqual((n1 * n1).shape, (t1 * t1).shape, f"Wrong shape when create tensor from int.")
+        self.assertEqual((n1 * n1).ndim, (t1 * t1).ndim, f"Wrong ndim when create tensor from int.")
+
+        n2 = np.array([8, 3, 4])
+        t2 = Tensor([8, 3, 4])
+
+        self.assertEqual((n1*n2).shape, (t1*t2).shape, f"Wrong shape when create tensor from int.")
+        self.assertEqual((n1*n2).ndim, (t1*t2).ndim, f"Wrong ndim when create tensor from int.")
+
+        self.assertEqual((n2*n1).shape, (t2*t1).shape, f"Wrong shape when create tensor from int.")
+        self.assertEqual((n2*n1).ndim, (t2*t1).ndim, f"Wrong ndim when create tensor from int.")