python词频分析

文本名：结果50.txt：

datasets labels testing used real test synthetic different contains
vol ieee pp analysis machine transactions pattern intelligence trans learning journal  acm  based
tree l2 l1 ac trees pc ab se template latent sm decision uk 70 go minutiae mc fr sw es
arxiv query preprint queries 2017 abs corr qi 2016 ba 2015 101 fo 2014 glass 2018 networks brox image recommendation
face x1 recognition facial faces database ii expression x2 age identity alignment person expressions verification sign sketch im identification wild
matrix xi vector denotes th denote norm matrices yi xt respectively element vectors tr elements operator covariance product let diagonal
bound complete theorem complexity problem lower upper np extension bounds whether arguments hard af bounded given stable argument ap regret
context event co events mod car cc contexts ut road modeling nd bridge aj activity els neuron ff 360
function space distance eq linear kernel loss functions defined objective parameter parameters using equation term two problem following vector non
object features feature detection level image objects segmentation region semantic local based regions box bounding spatial pooling detector cnn using
pp proc conf vis comput int 2014 vol recognit 2016 english 2011 pm intell springer inf det notes res syst
scale large language visual v1 v2 natural fei discovery mining u1 ng machines data support 2010 multi ijcv challenge machine
images dataset ground truth datasets data image real training synthetic contains different scenes world scene used annotations benchmark annotated categories
graph local edge matching nodes node global edges graphs cluster constraint clusters set two constraints similarity structure vertices pairwise tree
set error test training average mean score used values evaluation number data different table sets metrics testing validation performance results
learning deep neural networks wang li zhang liu 2016 2017 2018 pp chen convolutional yang 2015 processing image cvpr sun
system human systems user ai visual people information interaction understanding models uncertainty control applications research users data task gaze model
point points camera position direction surface rotation plane center outliers projection angle cloud two normal registration set orientation along positions
section paper experiments end results study described details following sec work experimental present main presented analysis detailed implementation see next
results performance method methods table proposed accuracy better art state comparison compared also baseline model best show fig shown datasets
model distribution probability random variable gaussian variables sampling distributions inference density models data prior likelihood bayesian mixture probabilities conditional variational
gradient block update convergence blocks vs stochastic algorithm updated least descent updates rs mm residual wavelet algo gradients greedy method
time algorithm number step cost search solution iteration instances optimal size complexity computation sat steps computational iterations polynomial total times
based methods problem approach proposed work method algorithms data learning approaches problems also propose optimization framework however algorithm related used
introduction pt ps gt 33 cl closure dp 200 400 ft lk sound nc 600 mt fs mp rational nm
domain class learning target label samples classification data classes source training task supervised labels adaptation cross classifier loss multi transfer
size rate binary batch memory 100 code normalization 64 codes quantization 128 256 learning length bit gpu 32 training epochs
al et rain proposed wang removal zhang li liu methods recently based chen streaks recent yang zhu deraining works introduced
let set proof every since definition lemma rule follows consider following thus proposition theorem case formula true one assume holds
one may however case also would even thus small could since possible number due example cases large two information different
table input adversarial loss output image generation generative generated gan decoder translation images encoder generator generate style discriminator s1 f1
fig figure shown right shows left example top row line results see bottom two red false show lines examples vi
part attributes attribute work supported ni grant material stereo project version authors w1 photometric fixation fc w2 water duration media
set logic knowledge relation model semantics reasoning models program rules epistemic relations sets base properties answer abstract formulas programs interpretation
explanation text explanations question word words questions open sentence zi answer xk post answers participants concepts document answering intention gadget
data rank sparse clustering low representation dimensional tensor subspace si bi ri spectral dictionary pca high sparsity completion recovery s0
video motion view frame frames temporal depth sequence sequences videos flow camera optical views time rgb using tracking dynamic cameras
online tracking term association long exp available 32 ti short graphics landmark lda 55 else 49 tracker pn interactive po
intelligence artificial 40 30 50 60 2021 ci 2019 80 300 35 2020 aaai 100 27 00 ui 20 150
university research science computer degree engineering received technology china interests department institute mail include ph vision currently professor learning school
3d pose shape estimation 2d model reconstruction body human head poses using images shapes landmarks depth hand appearance surface based
image images resolution map noise pixel pixels light high depth color maps quality super low using field filter channel saliency
de index ieee di terms member information 1000 2000 org sk sa la 500 http dk requires dx www publications
action state actions planning states plan agent goal initial policy appendix pr transition robot observation observations set belief reward goals
network layer model deep training layers learning trained networks cnn architecture neural convolutional attention two pre input feature end models
log path st references change paths return ij y1 ad inequality publication accepted ds worker hm final xn wk information
pp proceedings conference international vol journal theory acm max pages press springer xj volume systems statistics new 2007 programming statistical
agents agent group game groups player games strategy utility value p1 social p2 players items allocation communication cs preferences ln
vision computer conference ieee recognition pattern pp international proceedings cvpr 2015 image 2017 2016 iccv european 2018 2013 pages object
supplementary embeddings dp lp text english w2 expansion document sentences dx languages

源代码:

from pyecharts import options as opts
from pyecharts.charts import Bar

def gettext():
    txt = open("结果50.txt","r",errors='ignore').read()
    txt = txt.lower()
    for ch in '!"#$&()*+,-./:;<=>?@[\\]^_{|}·~‘’':
        txt = txt.replace(ch,"")
    return txt
txt = gettext()
words = txt.split()
print(words)
counts = {}
for word in words:
    counts[word] = counts.get(word,0) + 1

items = list(counts.items())
items.sort(key=lambda x:x[1],reverse=True)
# print(items)
words = []
counts = []
for i in range(20):
    words.append(items[i][0])
    counts.append(items[i][1])

# print(words)
# print(counts)
c = (
    Bar()
    .add_xaxis(words)
    .add_yaxis("次数", counts)
    .reversal_axis()
    .set_series_opts(label_opts=opts.LabelOpts(position="right"))
    .set_global_opts(title_opts=opts.TitleOpts(title="英文词出现字数"))
    .render("英文词出现字数.html")
)

效果展示：