Initial bits

README.md

@@ -1,4 +1,4 @@
-k-means---pandas
+k-means++-pandas
 ================
 
-An implementation of the k-means++ clustering algorithm using Pandas
+An implementation of the [k-means++ clustering algorithm](http:https://en.wikipedia.org/wiki/K-means%2B%2B) using [Pandas](http:https://pandas.pydata.org/)

k-means++.py

@@ -0,0 +1,37 @@
+from pandas import DataFrame,Series
+import pandas as pd
+import numpy as np
+
+class KMeansPlusPlus:
+ def __init__(self,dataFrame,columns=None,maxIterations=None):
+ if not isinstance(dataFrame,DataFrame):
+ raise Exception("dataFrame argument is not a pandas DataFrame")
+ elif dataFrame.empty:
+ raise Exception("The given data frame is empty")
+
+ self.dataFrame = dataFrame
+ self.numRows = dataFrame.shape[0]
+
+ if columns is None:
+ self.columns = dataFrame.columns
+ else:
+ for col in columns:
+ if col not in dataFrame.columns:
+ raise Exception("Column '%s' not found in the given DataFrame" % col)
+ if not self.__is_numeric(col):
+ raise Exception("The column '%s' is either not numeric or contains NaN values" % col)
+ self.columns = columns
+
+ def distance_from_point(self,point):
+ if not isinstance(point,np.array):
+ raise Exception("Argument '%s' is not a NumPy ndarray" % point)
+ elif point.ndim != 1:
+ raise Exception("One-dimensional points only, please.")
+ elif point.shape[0] != len(self.columns):
+ raise Exception("The point '%s' is not of the same dimension as the given set of columns" % point)
+
+ return np.power(self.dataFrame[columns] - point,2).sum(axis=1) #pandas Series
+
+
+ def __is_numeric(self,col):
+ return all(np.isreal(self.dataFrame[col])) and not any(np.isnan(self.dataFrame[col]))