build trigger

README.md

@@ -1,7 +1,7 @@
 # Chronos
 
 
-![pypi build](https://img.shields.io/github/workflow/status/h3x4g0ns/py-chronos/pypi-build)
+![Build Status](https://img.shields.io/github/actions/workflow/status/h3x4g0ns/py-chronos/python-publish.yml)
 [![PyPI version](https://badge.fury.io/py/py-quantize-chronos.svg)](https://badge.fury.io/py/py-quantize-chronos)
 
 ## About this Project

build/lib/chronos/__init__.py

@@ -0,0 +1 @@
+from .chronos import *

build/lib/chronos/chronos.py

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+from tqdm import tqdm
+import numpy as np
+import types, time
+
+# lookup table for the trajectories and their derivatives
+FXS = {
+  "$1$": lambda x: 1,
+  "$x$": lambda x: x,
+  "$x^2$": lambda x: x**2,
+  "$x^3$": lambda x: x**3,
+  "$log(x)$": lambda x: np.log(x),
+  "$xlog(x)$": lambda x: x*np.log(x),
+  "$2^x$": lambda x: 2**x
+}
+
+# num of iterations before we can potentially stop early
+EARLY_STOP = 20
+
+# main timer function 
+def timer(func: types.FunctionType, silent: bool=False, num: int=50):
+  # init variables for storage
+  x = np.arange(1, num+1)
+  y = []
+  # time the function
+  if not silent:
+    header = tqdm
+  else:
+    header = lambda x: x
+  for i in header(x):
+    start = time.time()
+    func(i)
+    end = time.time()
+    y.append(end-start)
+  loss, coeffs = [], []
+
+  # run regression for every trajectory
+  for func in FXS.values():
+    # find best fit via least squares
+    coeff = np.linalg.lstsq(np.array([func(i) for i in x]).reshape(-1, 1), y, rcond=None)[0]
+    coeffs.append(coeff)
+    # find total loss
+    cost = np.sum(np.abs(y - coeff*func(x)))
+    loss.append(cost)
+
+  # find the best fit
+  best = np.argmin(loss)
+  # return name of best fit and its coefficient
+  return list(FXS.keys())[best], coeffs[best]
+
+
+# empty function for testing functionality
+def test():
+  return "Hello World"

setup.cfg

@@ -1,6 +1,6 @@
 [metadata]
 name = py-quantize-chronos
-version = 0.1.7
+version = 0.1.8
 author = Ekansh Agrawal
 author_email = [email protected]
 description = utility tool that analyzes symbolic runtime of python functions