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removed junk files from homework 6

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1 parent a0f44df commit c9197a95b27589eaf57be5dac606c2e4b452ffd4 @sp0rus committed
Showing with 0 additions and 233 deletions.
  1. +0 −53 homework/homework6/hw6.py~
  2. +0 −151 homework/homework6/iris.data~
  3. +0 −29 homework/homework6/multivariate.py~
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53 homework/homework6/hw6.py~
@@ -1,53 +0,0 @@
-#!/usr/bin/env python
-
-import numpy as np
-import matplotlib.pyplot as plt
-import multivariate
-import math
-
-#read all the data in from the file
-sepallength = np.loadtxt('iris.data', delimiter=',', skiprows=0,usecols=[0])
-sepalwidth = np.loadtxt('iris.data', delimiter=',', skiprows=0,usecols=[1])
-petallength = np.loadtxt('iris.data', delimiter=',', skiprows=0,usecols=[2])
-petalwidth = np.loadtxt('iris.data', delimiter=',', skiprows=0,usecols=[3])
-irisclass = []
-for line in file('iris.data'):
- line = line.strip()
- curline = line.split(",")
- iristype = 0
- if curline[4] == 'Iris-setosa':
- iristype = 0
- elif curline[4] == 'Iris-versicolor':
- iristype = 1
- elif curline[4] == 'Iris-virginica':
- iristype = 2
- irisclass.append(iristype)
-irisclass = np.array(irisclass)
-
-# sepal length and petal length
-coeff = multivariate.multiRegression(sepallength, petallength)
-fit = multivariate.estimatedFit()
-rsquare = multivariate.adjustedRsquared()
-print "Regression coefficient: %s" %(coeff)
-print "Adjusted R-square Value: %s" %(rsquare)
-
-# sepal length and sepal width
-coeff = multivariate.multiRegression(sepallength, sepalwidth)
-fit = multivariate.estimatedFit()
-rsquare = multivariate.adjustedRsquared()
-print "Regression coefficient: %s" %(coeff)
-print "Adjusted R-square Value: %s" %(rsquare)
-
-# sepal length, petal length, petal width
-coeff = multivariate.multiRegression()
-fit = multivariate.estimatedFit()
-rsquare = multivariate.adjustedRsquared()
-print "Regression coefficient: %s" %(coeff)
-print "Adjusted R-square Value: %s" %(rsquare)
-
-# sepal length, sepal width, petal length, petal width
-coeff = multivariate.multiRegression()
-fit = multivariate.estimatedFit()
-rsquare = multivariate.adjustedRsquared()
-print "Regression coefficient: %s" %(coeff)
-print "Adjusted R-square Value: %s" %(rsquare)
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151 homework/homework6/iris.data~
@@ -1,151 +0,0 @@
-5.1,3.5,1.4,0.2,Iris-setosa
-4.9,3.0,1.4,0.2,Iris-setosa
-4.7,3.2,1.3,0.2,Iris-setosa
-4.6,3.1,1.5,0.2,Iris-setosa
-5.0,3.6,1.4,0.2,Iris-setosa
-5.4,3.9,1.7,0.4,Iris-setosa
-4.6,3.4,1.4,0.3,Iris-setosa
-5.0,3.4,1.5,0.2,Iris-setosa
-4.4,2.9,1.4,0.2,Iris-setosa
-4.9,3.1,1.5,0.1,Iris-setosa
-5.4,3.7,1.5,0.2,Iris-setosa
-4.8,3.4,1.6,0.2,Iris-setosa
-4.8,3.0,1.4,0.1,Iris-setosa
-4.3,3.0,1.1,0.1,Iris-setosa
-5.8,4.0,1.2,0.2,Iris-setosa
-5.7,4.4,1.5,0.4,Iris-setosa
-5.4,3.9,1.3,0.4,Iris-setosa
-5.1,3.5,1.4,0.3,Iris-setosa
-5.7,3.8,1.7,0.3,Iris-setosa
-5.1,3.8,1.5,0.3,Iris-setosa
-5.4,3.4,1.7,0.2,Iris-setosa
-5.1,3.7,1.5,0.4,Iris-setosa
-4.6,3.6,1.0,0.2,Iris-setosa
-5.1,3.3,1.7,0.5,Iris-setosa
-4.8,3.4,1.9,0.2,Iris-setosa
-5.0,3.0,1.6,0.2,Iris-setosa
-5.0,3.4,1.6,0.4,Iris-setosa
-5.2,3.5,1.5,0.2,Iris-setosa
-5.2,3.4,1.4,0.2,Iris-setosa
-4.7,3.2,1.6,0.2,Iris-setosa
-4.8,3.1,1.6,0.2,Iris-setosa
-5.4,3.4,1.5,0.4,Iris-setosa
-5.2,4.1,1.5,0.1,Iris-setosa
-5.5,4.2,1.4,0.2,Iris-setosa
-4.9,3.1,1.5,0.1,Iris-setosa
-5.0,3.2,1.2,0.2,Iris-setosa
-5.5,3.5,1.3,0.2,Iris-setosa
-4.9,3.1,1.5,0.1,Iris-setosa
-4.4,3.0,1.3,0.2,Iris-setosa
-5.1,3.4,1.5,0.2,Iris-setosa
-5.0,3.5,1.3,0.3,Iris-setosa
-4.5,2.3,1.3,0.3,Iris-setosa
-4.4,3.2,1.3,0.2,Iris-setosa
-5.0,3.5,1.6,0.6,Iris-setosa
-5.1,3.8,1.9,0.4,Iris-setosa
-4.8,3.0,1.4,0.3,Iris-setosa
-5.1,3.8,1.6,0.2,Iris-setosa
-4.6,3.2,1.4,0.2,Iris-setosa
-5.3,3.7,1.5,0.2,Iris-setosa
-5.0,3.3,1.4,0.2,Iris-setosa
-7.0,3.2,4.7,1.4,Iris-versicolor
-6.4,3.2,4.5,1.5,Iris-versicolor
-6.9,3.1,4.9,1.5,Iris-versicolor
-5.5,2.3,4.0,1.3,Iris-versicolor
-6.5,2.8,4.6,1.5,Iris-versicolor
-5.7,2.8,4.5,1.3,Iris-versicolor
-6.3,3.3,4.7,1.6,Iris-versicolor
-4.9,2.4,3.3,1.0,Iris-versicolor
-6.6,2.9,4.6,1.3,Iris-versicolor
-5.2,2.7,3.9,1.4,Iris-versicolor
-5.0,2.0,3.5,1.0,Iris-versicolor
-5.9,3.0,4.2,1.5,Iris-versicolor
-6.0,2.2,4.0,1.0,Iris-versicolor
-6.1,2.9,4.7,1.4,Iris-versicolor
-5.6,2.9,3.6,1.3,Iris-versicolor
-6.7,3.1,4.4,1.4,Iris-versicolor
-5.6,3.0,4.5,1.5,Iris-versicolor
-5.8,2.7,4.1,1.0,Iris-versicolor
-6.2,2.2,4.5,1.5,Iris-versicolor
-5.6,2.5,3.9,1.1,Iris-versicolor
-5.9,3.2,4.8,1.8,Iris-versicolor
-6.1,2.8,4.0,1.3,Iris-versicolor
-6.3,2.5,4.9,1.5,Iris-versicolor
-6.1,2.8,4.7,1.2,Iris-versicolor
-6.4,2.9,4.3,1.3,Iris-versicolor
-6.6,3.0,4.4,1.4,Iris-versicolor
-6.8,2.8,4.8,1.4,Iris-versicolor
-6.7,3.0,5.0,1.7,Iris-versicolor
-6.0,2.9,4.5,1.5,Iris-versicolor
-5.7,2.6,3.5,1.0,Iris-versicolor
-5.5,2.4,3.8,1.1,Iris-versicolor
-5.5,2.4,3.7,1.0,Iris-versicolor
-5.8,2.7,3.9,1.2,Iris-versicolor
-6.0,2.7,5.1,1.6,Iris-versicolor
-5.4,3.0,4.5,1.5,Iris-versicolor
-6.0,3.4,4.5,1.6,Iris-versicolor
-6.7,3.1,4.7,1.5,Iris-versicolor
-6.3,2.3,4.4,1.3,Iris-versicolor
-5.6,3.0,4.1,1.3,Iris-versicolor
-5.5,2.5,4.0,1.3,Iris-versicolor
-5.5,2.6,4.4,1.2,Iris-versicolor
-6.1,3.0,4.6,1.4,Iris-versicolor
-5.8,2.6,4.0,1.2,Iris-versicolor
-5.0,2.3,3.3,1.0,Iris-versicolor
-5.6,2.7,4.2,1.3,Iris-versicolor
-5.7,3.0,4.2,1.2,Iris-versicolor
-5.7,2.9,4.2,1.3,Iris-versicolor
-6.2,2.9,4.3,1.3,Iris-versicolor
-5.1,2.5,3.0,1.1,Iris-versicolor
-5.7,2.8,4.1,1.3,Iris-versicolor
-6.3,3.3,6.0,2.5,Iris-virginica
-5.8,2.7,5.1,1.9,Iris-virginica
-7.1,3.0,5.9,2.1,Iris-virginica
-6.3,2.9,5.6,1.8,Iris-virginica
-6.5,3.0,5.8,2.2,Iris-virginica
-7.6,3.0,6.6,2.1,Iris-virginica
-4.9,2.5,4.5,1.7,Iris-virginica
-7.3,2.9,6.3,1.8,Iris-virginica
-6.7,2.5,5.8,1.8,Iris-virginica
-7.2,3.6,6.1,2.5,Iris-virginica
-6.5,3.2,5.1,2.0,Iris-virginica
-6.4,2.7,5.3,1.9,Iris-virginica
-6.8,3.0,5.5,2.1,Iris-virginica
-5.7,2.5,5.0,2.0,Iris-virginica
-5.8,2.8,5.1,2.4,Iris-virginica
-6.4,3.2,5.3,2.3,Iris-virginica
-6.5,3.0,5.5,1.8,Iris-virginica
-7.7,3.8,6.7,2.2,Iris-virginica
-7.7,2.6,6.9,2.3,Iris-virginica
-6.0,2.2,5.0,1.5,Iris-virginica
-6.9,3.2,5.7,2.3,Iris-virginica
-5.6,2.8,4.9,2.0,Iris-virginica
-7.7,2.8,6.7,2.0,Iris-virginica
-6.3,2.7,4.9,1.8,Iris-virginica
-6.7,3.3,5.7,2.1,Iris-virginica
-7.2,3.2,6.0,1.8,Iris-virginica
-6.2,2.8,4.8,1.8,Iris-virginica
-6.1,3.0,4.9,1.8,Iris-virginica
-6.4,2.8,5.6,2.1,Iris-virginica
-7.2,3.0,5.8,1.6,Iris-virginica
-7.4,2.8,6.1,1.9,Iris-virginica
-7.9,3.8,6.4,2.0,Iris-virginica
-6.4,2.8,5.6,2.2,Iris-virginica
-6.3,2.8,5.1,1.5,Iris-virginica
-6.1,2.6,5.6,1.4,Iris-virginica
-7.7,3.0,6.1,2.3,Iris-virginica
-6.3,3.4,5.6,2.4,Iris-virginica
-6.4,3.1,5.5,1.8,Iris-virginica
-6.0,3.0,4.8,1.8,Iris-virginica
-6.9,3.1,5.4,2.1,Iris-virginica
-6.7,3.1,5.6,2.4,Iris-virginica
-6.9,3.1,5.1,2.3,Iris-virginica
-5.8,2.7,5.1,1.9,Iris-virginica
-6.8,3.2,5.9,2.3,Iris-virginica
-6.7,3.3,5.7,2.5,Iris-virginica
-6.7,3.0,5.2,2.3,Iris-virginica
-6.3,2.5,5.0,1.9,Iris-virginica
-6.5,3.0,5.2,2.0,Iris-virginica
-6.2,3.4,5.4,2.3,Iris-virginica
-5.9,3.0,5.1,1.8,Iris-virginica
-
View
29 homework/homework6/multivariate.py~
@@ -1,29 +0,0 @@
-import numpy as np
-
-def multiRegression(IDP, DEP):
- assert np.size(IDP[:, 0]) == np.size(DEP[:, 0])
- assert np.size(DEP[0, :] == 1)
- records = np.size(IND[:, 0])
- IND = np.concatonate(IND, np.ones(records).reshape(records, 1), axis = 1)
- return np.linalg.solve(np.dot(IND.transpose(), IDP), np.dot(IND.transpose()), DEP.reshape(records, 1))
-
-def estimatedFit(IND, coeff):
- records = np.size(IND[:, 0])
- IND = np.concatonate(IND, np.ones(records).reshape(records, 1), axis = 1)
- coeff = np.repeat(coeff.transpose(), record, axis = 0)
- return np.sum(IND * coeff, axis = 1).reshape(records, 1)
-
-def corr(FIT, Y):
- muFit = sum(FIT)/float(len(FIT))
- muy = sum(Y)/float(len(Y))
- X0 = FIT - muFit
- Y0 = Y - muy
- return sum(X0 * Y0)/np.sqrt(sum(X0 * X0) * sum(Y0 * Y0)
-
-def adjustedRsquared(FIT, Y, variables):
- assert np.size(FIT) == np.size(Y)
- records = np.size(FIT[:, 0])
- muy = sum(Y)/float(len(Y))
- E = (Y - FIT) * (Y - FIT)
- YY = (Y - muy) * (Y - muy)
- return 1 - ((sum(E)/float(records-variables-1))/sum(YY)/float(records-1))

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