-za-z]"," ", Sent.lower (). Strip ()). Split () in sentences.append (temp) - to returnsentences + - #The sentences in the long news are stripped out for training . thesentences = [] * forIinchx: $Sentence_list =news_to_sentences (i)Panax NotoginsengSentences + =sentence_list - the + #Configure the dimension of the word vector ANum_features = 300 the #the frequency of the words that are to be considered +Min_word_count = 20 - #number of CPU cores used in parallel computing $Num_workers =
#岭回归主要是弥补在数据中出现异常值时, improve the stability of linear model, that is, robustness robustImport Pandas as PDImport NumPy as NPImport Matplotlib.pyplot as PltFrom Sklearn import Linear_modelImport Sklearn.metrics as SM#直接拿最小二乘法数据Ridgerg=linear_model. Ridge (alpha=0.5,fit_intercept=true,max_iter=10000) #alpha nearer to 0, the more the ridge regression approached the linear regression.Ridgerg.fit (X_train,y_train) #训练模型Y_train_pred=ridgerg.predict (X_train) #模型y值Y_test_pred=ridgerg.predict (x_test) #模
Python Kmeans clustering is relatively simple, first requires the import NumPy, from the Sklearn.cluster import Kmeans module:Import NumPy as NP from Import KmeansThen read the TXT file, get the corresponding data and convert it to numpy array:X == open ('rktj4.txt') for in f: = Re.compile ('\s+') x.append ([Float (Regex.Split (v) [3]), float ( Regex.Split (v) [6= Np.array (X)Set the number of classes and cluster:N_clusters = 5= Kmeans (n_clust
Maximumlikelihood (p=w): H,t=defineparam () f1=factorial (h+t)/(factorial (H) *factorial (T)) f2= (p**h) * ((1.0-p) **t) return F1*F2 def factorial (x): return reduce (lambda x,y:x*y,range (1,x+1)) achieve the effect, corresponding to the above example, when h=49,t=31, is the probability of P=2/3 probabilitiesCode Address: Please click on my/********************************* This article from the blog "Bo Li Garvin"* Reprint Please indicate the sourc
It is mentioned in this series that using Python to start machine learning (3: Data fitting and generalized linear regression) mentions the regression algorithm for numerical prediction. The logical regression algorithm is essentially regression, but it introduces a logical function to help classify it. The practice found that the logical regression in the field
Preface: "The foundation determines the height, not the height of the foundation!" The book mainly from the coding program, data structure, mathematical theory, data processing and visualization of several aspects of the theory of machine learning, and then extended to the probability theory, numerical analysis, matrix analysis and other knowledge to guide us int
Python implements simplified address book modification and python address book modification
Description:
I wrote a simple address book in my previous blog, but I still think it is not perfect:
You need to enter the ID. Although the ID is the primary key, the auto-increment f
acceleration.
gensim-Theme Modeling Tools.
pybrain-Another machine learning library.
crab-extensible, fast recommendation engine.
Python-recsys-python implementation of the recommended system.
Thinking bayes-'s book on Bayesian analysis
Restricted Bo
algorithms that can be used to allow programmers to experiment with tools and libraries of programming functions. The most representative of the book is: "Programming collective Intelligence", "Machine learning for Hackers", "Hackersand Data mining:practical Machine learning
Learning notes for "Machine Learning Practice": Implementation of k-Nearest Neighbor algorithms, and "Machine Learning Practice" k-
The main learning and research tasks of the last semester were pattern recognition, signal theor
Machine learning and its application 2013 content introduction BooksComputer BooksMachine learning is a very important area of research in computer science and artificial intelligence. In recent years, machine learning has not only been a great skill in many fields of comput
simple printing has been taught to complete project implementation, so that beginners start from the basic programming technology, and finally experience the basic process of software development."Daniel Evaluation" Hardway (Stupid method) is more suitable for starting programming, as a beginner of Python is very good.Fourth a feifanyuyule.cn haiyuanylpt.com yihuangylpt.cn yigouylpt2.comHere we recommend the last "collective intelligence programming"
learning to organize the daily learning of machine learning algorithms, and practical problems, do more experiments, and strive to get a better learning effect, I will be firm belief, more efforts to catch up with the pace of excellence.Reprint please indicate the author Ja
wrong classification point is not, then the value of the loss function is definitely 0.The Perceptual machine learning algorithm is driven by mis-classification and adopts random gradient descent method. First, arbitrarily select a super-planar w,b and then minimize the target function. The definitions are given in the author's book. Not a wordy.The original for
sample demonstrations and exercises
Includes advanced features in Python
This book is self-taught and programmed into Microsoft, more than 30 years of programming experience, and how to make it easier for readers to learn programming skills.Cia Qingcai
Watercress Rating 9.2
Millions of Visitors blog author works
The most popular reptile in the audience
John v. GuttagTranslato
images in Python, which has a pretty good effect.
SVG chart builder in pygal-Python.
Pycascading
Miscellaneous scripts/ipython notes/code library
Pattern_classification
Thinking stats 2
Hyperopt
Numpic
2012-paper-diginorm
Ipython-notebooks
Demo-weights
Sarah Palin lda-Sarah Palin's email about topic modeling.
Diffusion segmentation-a set of image segmentation algorithms based on the diffusion m
Hyperopt
Numpic
2012-paper-diginorm
Ipython-notebooks
Demo-weights
Sarah Palin lda-Sarah Palin's email about topic modeling.
Diffusion segmentation-a set of image segmentation algorithms based on the diffusion method.
Scipy tutorials-scipy tutorial. It is out of date. Please refer to scipy-lecture-notes
Crab-Python recommendation engine library.
Bayesian inference tool in bayespy-Python.
Scikit-
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