Numpy包
mat( )函數(shù)可以將數(shù)組轉(zhuǎn)化為矩陣
randMat=mat(random.rand(4,4))
.I表示求逆
randMat.I
from numpy import *
import operator
tile用法：
Signature:?tile(A,?reps)Docstring:
Construct?an?array?by?repeating?A?the?number?of?times?given?by?reps.
If?`reps`?has?length?``d``,?the?result?will?have?dimension?of
``max(d,?A.ndim)``.
If?``A.ndim?<?d``,?`A`?is?promoted?to?be?d-dimensional?by?prepending?new
axes.?So?a?shape?(3,)?array?is?promoted?to?(1,?3)?for?2-D?replication,
or?shape?(1,?1,?3)?for?3-D?replication.?If?this?is?not?the?desired
behavior,?promote?`A`?to?d-dimensions?manually?before?calling?this
function.
If?``A.ndim?>?d``,?`reps`?is?promoted?to?`A`.ndim?by?pre-pending?1's?to?it.
Thus?for?an?`A`?of?shape?(2,?3,?4,?5),?a?`reps`?of?(2,?2)?is?treated?as
(1,?1,?2,?2).
Note?:?Although?tile?may?be?used?for?broadcasting,?it?is?strongly
recommended?to?use?numpy's?broadcasting?operations?and?functions.
Parameters
----------
A?:?array_like
The?input?array.
reps?:?array_like
The?number?of?repetitions?of?`A`?along?each?axis.
Returns
-------
c?:?ndarray
The?tiled?output?array.
See?Also
--------
repeat?:?Repeat?elements?of?an?array.
broadcast_to?:?Broadcast?an?array?to?a?new?shape
Examples
--------
>>>?a?=?np.array([0,?1,?2])
>>>?np.tile(a,?2)
array([0,?1,?2,?0,?1,?2])
>>>?np.tile(a,?(2,?2))
array([[0,?1,?2,?0,?1,?2],
[0,?1,?2,?0,?1,?2]])
>>>?np.tile(a,?(2,?1,?2))
array([[[0,?1,?2,?0,?1,?2]],
[[0,?1,?2,?0,?1,?2]]])
>>>?b?=?np.array([[1,?2],?[3,?4]])
>>>?np.tile(b,?2)
array([[1,?2,?1,?2],
[3,?4,?3,?4]])
>>>?np.tile(b,?(2,?1))
array([[1,?2],
[3,?4],
[1,?2],
[3,?4]])
>>>?c?=?np.array([1,2,3,4])
>>>?np.tile(c,(4,1))
array([[1,?2,?3,?4],
[1,?2,?3,?4],
[1,?2,?3,?4],
[1,?2,?3,?4]])
File:? ? ? d:\anaconda3\lib\site-packages\numpy\lib\shape_base.py
Type:? ? ? function
argsort用法：
Signature:?argsort(a,?axis=-1,?kind='quicksort',?order=None)Docstring:
Returns?the?indices?that?would?sort?an?array.
Perform?an?indirect?sort?along?the?given?axis?using?the?algorithm?specified
by?the?`kind`?keyword.?It?returns?an?array?of?indices?of?the?same?shape?as
`a`?that?index?data?along?the?given?axis?in?sorted?order.
Parameters
----------
a?:?array_like
Array?to?sort.
axis?:?int?or?None,?optional
Axis?along?which?to?sort.? The?default?is?-1?(the?last?axis).?If?None,
the?flattened?array?is?used.
kind?:?{'quicksort',?'mergesort',?'heapsort'},?optional
Sorting?algorithm.
order?:?str?or?list?of?str,?optional
When?`a`?is?an?array?with?fields?defined,?this?argument?specifies
which?fields?to?compare?first,?second,?etc.? A?single?field?can
be?specified?as?a?string,?and?not?all?fields?need?be?specified,
but?unspecified?fields?will?still?be?used,?in?the?order?in?which
they?come?up?in?the?dtype,?to?break?ties.
Returns
-------
index_array?:?ndarray,?int
Array?of?indices?that?sort?`a`?along?the?specified?axis.
If?`a`?is?one-dimensional,?``a[index_array]``?yields?a?sorted?`a`.
See?Also
--------
sort?:?Describes?sorting?algorithms?used.
lexsort?:?Indirect?stable?sort?with?multiple?keys.
ndarray.sort?:?Inplace?sort.
argpartition?:?Indirect?partial?sort.
Notes
-----
See?`sort`?for?notes?on?the?different?sorting?algorithms.
As?of?NumPy?1.4.0?`argsort`?works?with?real/complex?arrays?containing
nan?values.?The?enhanced?sort?order?is?documented?in?`sort`.
Examples
--------
One?dimensional?array:
>>>?x?=?np.array([3,?1,?2])
>>>?np.argsort(x)
array([1,?2,?0])
Two-dimensional?array:
>>>?x?=?np.array([[0,?3],?[2,?2]])
>>>?x
array([[0,?3],
[2,?2]])
>>>?np.argsort(x,?axis=0)
array([[0,?1],
[1,?0]])
>>>?np.argsort(x,?axis=1)
array([[0,?1],
[0,?1]])
Sorting?with?keys:
>>>?x?=?np.array([(1,?0),?(0,?1)],?dtype=[('x',?'
>>>?x
array([(1,?0),?(0,?1)],
dtype=[('x',?'
>>>?np.argsort(x,?order=('x','y'))
array([1,?0])
>>>?np.argsort(x,?order=('y','x'))
array([0,?1])
File:? ? ? d:\anaconda3\lib\site-packages\numpy\core\fromnumeric.py
Type:? ? ? function
?operator.itemgetter：
Init?signature:?operator.itemgetter(self,?/,?*args,?**kwargs)Docstring:
itemgetter(item,?...)?-->?itemgetter?object
Return?a?callable?object?that?fetches?the?given?item(s)?from?its?operand.
After?f?=?itemgetter(2),?the?call?f(r)?returns?r[2].
After?g?=?itemgetter(2,?5,?3),?the?call?g(r)?returns?(r[2],?r[5],?r[3])
File:? ? ? ? ? ?d:\anaconda3\lib\operator.py
Type:? ? ? ? ? ?type
sorted：
Signature:?sorted(iterable,?/,?*,?key=None,?reverse=False)Docstring:
Return?a?new?list?containing?all?items?from?the?iterable?in?ascending?order.
A?custom?key?function?can?be?supplied?to?customize?the?sort?order,?and?the
reverse?flag?can?be?set?to?request?the?result?in?descending?order.
Type:? ? ? builtin_function_or_method
?classCount={}
花括號表示字典
? ? for i in range(3):
? ? ? ? voteIlabel = labels[sortedDistIndicies[i]]
? ? ? ? classCount[voteIlabel] = classCount.get(voteIlabel,0)+1
AttributeError: 'dict' object has no attribute 'iteritems'
Python3.5中：iteritems變?yōu)閕tems
===============
文件讀取
def file2matrix(filename):
? ? fr = open(filename)
? ? arrayOlines=fr.readlines()
? ? numberOfLines = len(arrayOlines)
? ? returnMat = zeros((numberOfLines,3))
? ? classLabelVector = []
? ? index = 0
? ? for line in arrayOlines:
? ? ? ? line = line.strip()
? ? ? ? listFromLine = line.split('\t')
? ? ? ? returnMat[index,:]=listFromLine[0:3]
? ? ? ? classLabelVector.append(int(listFromLine[-1]))
? ? ? ? index += 1
? ? return returnMat,classLabelVector
line = line.strip()：截掉回車符
==================================
使用Matplotlib制作原始數(shù)據(jù)的散點圖：
import matplotlib
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(datingDataMat[:,1],datingDataMat[:,2])
plt.show()
=========
ax.scatter(datingDataMat[:,1],datingDataMat[:,2],15.0*array(datingLabels),15.0*array(datingLabels))
使區(qū)分
==============================
def autoNorm(dataSet):
? ? minVals = dataSet.min(0)
? ? maxVals = dataSet.max(0)
? ? ranges = maxVals - minVals
? ? normDataSet = zeros(shape(dataSet))
? ? m = dataSet.shape[0]
? ? normDataSet = dataSet - tile(minVals, (m,1))
? ? normDataSet = normDataSet/tile(ranges,(m,1))
? ? return normDataSet,ranges,minVals
============
normDataSet = normDataSet/tile(ranges,(m,1))不是矩陣除法，在NumPy庫中紊搪，矩陣除法需要使用函數(shù)linalg.solve(matA,matB)
========
reload:
import importlib
importlib.reload(kNN)
=================================
