release version 0.3.0

Author: Orcuslc

README.md

@@ -1,5 +1,5 @@
 # OrthNet
-TensorFlow and PyTorch layers for generating multi-dimensional Orthogonal Polynomials
+TensorFlow, PyTorch and Numpy layers for generating multi-dimensional Orthogonal Polynomials
 
 ## Installation:
 1. the stable version:  
@@ -13,7 +13,7 @@ python3 setup.py build_ext --inplace && python3 setup.py install
 
 ---
 ## Orthogonal polynomials supported:
-- [Legendre polynomial](https://en.wikipedia.org/wiki/Legendre_polynomials)
+- [Legendre polynomial](https://en.wikipedia.org/wiki/Legendre_polynomials) with Normalized Legendre polynomial
 - [Laguerre polynomial](https://en.wikipedia.org/wiki/Laguerre_polynomials)
 - [Hermite polynomial](https://en.wikipedia.org/wiki/Hermite_polynomials)
   - hermite: He(x) in Wiki, the poly in probability theory
@@ -24,14 +24,32 @@ python3 setup.py build_ext --inplace && python3 setup.py install
 - [Jacobi polynomial](https://en.wikipedia.org/wiki/Jacobi_polynomials)
 
 ---
-## Usage: check `demo/` folder.
 
-## Attributes:
-Class `Poly`:
+## Classes:
+- orthnet.Legendre(Poly)
+- orthnet.Legendre_Normalized(Poly)
+- orthnet.Laguerre(Poly)
+- orthnet.Hermite(Poly)
+- orthnet.Hermite2(Poly)
+- orthnet.Chebyshev(Poly)
+- orthnet.Chebyshev2(Poly)
+- orthnet.Jacobi(Poly)
+
+## Base class:
+Class `Poly(module, degree, x, dtype = 'float32', loglevel = 0)`:
+
+- Inputs:
+    + module: one of {'tensorflow', 'pytorch', 'numpy'}
+    + degree: the highest degree of target polynomial
+    + x: input tensor of type {tf.placaholder, tf.Variable, torch.Variable, torch.Tensor, numpy.ndarray, numpy.matrix}
+    + dtype: 'float32' or 'float64'
+    + loglevel: 1 to print time cost and 0 to mute
+
 - `Poly.tensor`: return a tensor of function values
 - `Poly.combination`: return the combination of dimensions, in lexicographical order
 - `Poly.index`: return the index of the first combination of each degree in `self.combination`
 - `Poly.update(degree)`: update the degree of polynomial
 - `Poly.get_combination(start, end):`: return the combination of degrees from `start`(included) till `end`(included)
 - `Poly.get_poly(start, end)`: return the polynomials of degrees from `start`(included) till `end`(included)
 - `Poly.eval(coefficients)`: evaluate the value of polynomial with coefficients
+- `Poly.quadrature(func, weight)`: evaluate Gauss quadrature with target function and weights

orthnet/utils/timeit.py

@@ -0,0 +1,18 @@
+import time
+from functools import wraps
+
+def timeit(loglevel):
+	def _timeit(func):
+		@wraps(func)
+		def timed(*args, **kw):
+			t1 = time.time()
+			res = func(*args, **kw)
+			print(func.__name__, (time.time() - t1))
+			return res
+		def untimed(*args, **kw):
+			return func(*args, **kw)
+		if loglevel == 0:
+			return untimed
+		elif loglevel == 1:
+			return timed
+	return _timeit

setup.py

@@ -13,9 +13,9 @@
 
 setup(
 	name = 'orthnet',
-	version = '0.2.2',
-	keywords = ['orthogonal polynomial', 'tensorflow', 'pytorch'],
-	description = 'TensorFlow and PyTorch layers for generating orthogonal polynomials',
+	version = '0.3.0',
+	keywords = ['orthogonal polynomial', 'tensorflow', 'pytorch', 'numpy'],
+	description = 'TensorFlow, PyTorch and Numpy layers for generating orthogonal polynomials',
 	license = 'MIT',
 	author = 'Chuan Lu',
 	author_email = '[email protected]',