from sklearn import datasets
import numpy as np
# Assigning the petal length and petal width of the 150 flower samples to Matrix X
# Class labels of the flower to vector y
iris = datasets.load_iris()
X = iris.data[:, [2, 3]]
y = iris.target
print('Class labels:', np.unique(y))
from sk
我遇到了这个错误。我认为这是我当地设置的一个问题。
# Importing RFE and LinearRegression
from sklearn.feature_selection import RFE
from sklearn.linear_model import LinearRegression
# Running RFE with the output number of the variable equal to 10
lm = LinearRegression()
lm.fit(X_train, y_train)
rfe = RFE(lm, 10)
我正在使用自己的预测器,并希望像使用任何scikit例程一样使用它(例如RandomForestRegressor)。我有一个包含fit和predict方法的类,它们似乎工作得很好。但是,当我尝试使用一些scikit方法时,例如交叉验证,我得到如下错误:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Python27\lib\site-packages\sklearn\cross_validation.py", lin
我试图用权重选项计算集群的中心位置。但体重似乎不起作用。
下面是表示问题的简单脚本
X = []
weights = []
for x in range(-10,10):
for y in range(-10,10):
X+= [[x,y]]
if x>0 and y>0:
weights += [10000]
else:
weights += [1]
X = np.array(X)
weights = np.array(weights)
kmeans = KMeans(n_
我想使用准确率、精确度、召回率和F-measure作为性能度量。在只考虑准确性的情况下,代码工作得很好,但是当有很多指标时,我会得到错误。我想知道我怎样才能做到这一点?
import matplotlib.pyplot as plt
from sklearn import model_selection
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassif
我试图计算tf-idf,下面是我的代码:
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from nltk.corpus import stopwords
import numpy as np
import numpy.linalg as LA
train_set = ["The sky is blue.", "The sun is bright."] #Docume
在python 2.6.6中运行代码时,我得到了这个错误。在Python3.4.3中运行时没有问题
usr/lib64/python2.6/site-packages/sklearn/feature_selection/univariate_selection.py:319: UserWarning: Duplicate scores. Result may depend on feature ordering.There are probably duplicate features, or you used a classification score for a regression t
我有一个Pandas DataFrame,列包含x、y和z-值.
import pandas as pd
df = pd.DataFrame({'Age': x,
'Mileage': y,
'Price': z})
使用scipy.optimize.curvefit(),我能够拟合一个单变量指数函数y = exp(-bx):
import numpy as np
from scipy.optimize import curve_fit
# fit y to x
def
我正在尝试实现情感分析的支持向量机,我试图实现这个gitlink 。
from sklearn.model_selection import ShuffleSplit
from sklearn.model_selection import StratifiedKFold
我引用了它,因为它说要将交叉原点更改为model_selection,因为它是废弃的,所以我将其替换为
grid_svm = GridSearchCV(
pipeline_svm, #object used to fit the data
param_grid=param_svm,
refit=Tru
在试图构建Django API时,我总是会遇到错误。
我有一节课:
from uuid import UUID
from django.shortcuts import render
from django.http.response import JsonResponse
from django.http.request import HttpRequest
from rest_framework import viewsets, status
from rest_framework.parsers import JSONParser
from rest_framework.response
虽然使用木星笔记本,我从来没有这个问题的fit()功能。但有了这段代码,我会:
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
X = data.drop(columns=['Survived'])
y = data['Survived']
model = DecisionTreeClassifier
m
有人能解释一下为什么这个代码:
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import StratifiedKFold
from sklearn.feature_selection import SelectKBest
#from xgboost import XGBClassifier
from sklearn.feature_selection import mutual_info_classif
from sklearn.feature_selection impo
我正在使用sklearn将SVM拟合到一些数据。我总共有24个样本(10个阴性,14个阳性)。 # Set model
clf = svm.SVC(kernel = 'linear', C = 1)
# Create train, test splits and fit SVM to data
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 0, test_size = 0.3, stratify = y)
clf.fit(X_train, y_train)
# Make p
我试图在Python中运行带有外生输入的非线性自回归(NARX)。
这是我的密码
步骤1:导入库
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sysidentpy.model_structure_selection import FROLS
from sysidentpy.basis_function import Polynomial, Fourier
from sysidentpy.metrics import root_relative_squared_error
from sys
我有一个很大的表格,我根据它们的日期将它们分成了许多小表格: dfs={}
for fecha in fechas:
dfs[fecha]=df[df['date']==fecha].set_index('Hour')
#now I can acess the tables like this:
dfs['2019-06-23'].head() 我对dfs做了一些修改'2019-06-23‘具体的表,现在我想把它保存在我的计算机上。我试着用两种方法来做到这一点: #first try:
dfs['2019-06-23
我试图使用典型相关分析。尽管如此,我还是得到了一个断言TypeError的inverse_transform() takes 2 positional arguments but 3 were given。
这是我的代码:
from sklearn.ensemble import RandomForestRegressor
from sklearn.cross_decomposition import CCA
from sklearn.model_selection import RandomizedSearchCV
mycca = CCA(n_components=4)
mycca.fi
我目前正在从事“法国汽车索赔数据集freMTPL2freq”Kaggle竞赛()。不幸的是,当我使用NotFittedError时,我得到了一个“RandomizedSearchCV :所有估值器都无法拟合”的错误,并且我无法弄清楚为什么是这样。任何帮助都是非常感谢的。
import numpy as np
import statsmodels.api as sm
import scipy.stats as stats
from matplotlib import pyplot as plt
from sklearn.pipeline import Pipeline
from sklearn.