 from sklearn.datasets import load_iris
from sklearn.preprocessing import LabelEncoder
import numpy as np
# 加载数据集（这里以Iris数据集为例）
data =load_iris()
X = data.data
y = data.target
# 转换标签为数字（如果标签为字符串）
# label_encoder=LabelEncoder()
# y=label_encoder.fit_transform(y)

# 1. 计算数据样本本集中每个类别样本的均值
class_labels = np.unique(y)
mean_vectors=[]
for label in class_labels:
  mean_vectors.append(np.mean(X[y==label],axis=0))

# 2. 计算类内散度矩阵Sw和类间散度矩阵Sb
d = X.shape[1] #特征数
n = X.shape[0] #样本总数
k = len(class_labels) #类别数
# 类内散度矩阵Sw
Sw = np.zeros((d,d))
for label,mean_vec in zip(class_labels,mean_vectors):
  class_scatter = np.zeros((d,d))
  class_samples= X[y==label]
  for sample in class_samples:
    sample = sample.reshape(d,1) # 转化为列向量
    mean_vec = mean_vec.reshape(d,1) # 同上
    class_scatter += (sample - mean_vec).dot((sample - mean_vec).T)
  Sw += class_scatter
# 类间散度矩阵sb
mean_all= np.mean(X,axis=0).reshape(d,1)
Sb = np.zeros((d,d))
for label,mean_vec in zip(class_labels,mean_vectors):
    n_i=X[y==label].shape[0]
    mean_vec = mean_vec.reshape(d,1)
    Sb += n_i *(mean_vec - mean_all).dot((mean_vec - mean_all).T)

# 3.求解 Sw^-1 * Sb的特征向量
eigvals,eigvecs=np.linalg.eig(np.linalg.inv(Sw).dot(Sb))
# 按照特征值的大小排序特征向量
sorted_indices=np.argsort(eigvals)[::-1]
eigvecs_sorted=eigvecs[:,sorted_indices]

# 4. 选择前r个特征向量
W=eigvecs_sorted[:,:2] #选择前两个特征向量

# 5. 将每个样本映射到低维空间
X_lda= X.dot(W)
# 输出降维后的结果
print("降维后的数据：")
print(X_lda)

created 6 days ago

Python Online Compiler

Write, Run & Share Python code online using OneCompiler's Python online compiler for free. It's one of the robust, feature-rich online compilers for python language, supporting both the versions which are Python 3 and Python 2.7. Getting started with the OneCompiler's Python editor is easy and fast. The editor shows sample boilerplate code when you choose language as Python or Python2 and start coding.

Taking inputs (stdin)

OneCompiler's python online editor supports stdin and users can give inputs to programs using the STDIN textbox under the I/O tab. Following is a sample python program which takes name as input and print your name with hello.

import sys
name = sys.stdin.readline()
print("Hello "+ name)

About Python

Python is a very popular general-purpose programming language which was created by Guido van Rossum, and released in 1991. It is very popular for web development and you can build almost anything like mobile apps, web apps, tools, data analytics, machine learning etc. It is designed to be simple and easy like english language. It's is highly productive and efficient making it a very popular language.

Tutorial & Syntax help

Loops

1. If-Else:

When ever you want to perform a set of operations based on a condition IF-ELSE is used.

if conditional-expression
    #code
elif conditional-expression
    #code
else:
    #code

Note:

Indentation is very important in Python, make sure the indentation is followed correctly

2. For:

For loop is used to iterate over arrays(list, tuple, set, dictionary) or strings.

Example:

mylist=("Iphone","Pixel","Samsung")
for i in mylist:
    print(i)

3. While:

While is also used to iterate a set of statements based on a condition. Usually while is preferred when number of iterations are not known in advance.

while condition  
    #code

Collections

There are four types of collections in Python.

1. List:

List is a collection which is ordered and can be changed. Lists are specified in square brackets.

Example:

mylist=["iPhone","Pixel","Samsung"]
print(mylist)

2. Tuple:

Tuple is a collection which is ordered and can not be changed. Tuples are specified in round brackets.

Example:

myTuple=("iPhone","Pixel","Samsung")
print(myTuple)

Below throws an error if you assign another value to tuple again.

myTuple=("iPhone","Pixel","Samsung")
print(myTuple)
myTuple[1]="onePlus"
print(myTuple)

3. Set:

Set is a collection which is unordered and unindexed. Sets are specified in curly brackets.

Example:

myset = {"iPhone","Pixel","Samsung"}
print(myset)

4. Dictionary:

Dictionary is a collection of key value pairs which is unordered, can be changed, and indexed. They are written in curly brackets with key - value pairs.

Example:

mydict = {
    "brand" :"iPhone",
    "model": "iPhone 11"
}
print(mydict)

Supported Libraries

Following are the libraries supported by OneCompiler's Python compiler

Name	Description
NumPy	NumPy python library helps users to work on arrays with ease
SciPy	SciPy is a scientific computation library which depends on NumPy for convenient and fast N-dimensional array manipulation
SKLearn/Scikit-learn	Scikit-learn or Scikit-learn is the most useful library for machine learning in Python
Pandas	Pandas is the most efficient Python library for data manipulation and analysis
DOcplex	DOcplex is IBM Decision Optimization CPLEX Modeling for Python, is a library composed of Mathematical Programming Modeling and Constraint Programming Modeling