我有这样的档案:
head logistic_results.assoc_3.logistic
CHR SNP BP A1 TEST NMISS OR STAT P
2 2:129412140:T:C 129412140 C ADD 1438 1.523 3.89 0.0001004
15 15:26411414:G:A 26411414 A ADD 1438
我试图实现逻辑回归,但我收到了错误的阴谋。
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn import cross_validation
from sklearn.linear_model import LogisticRegression
sns.set()
x = (np.random.randint(2000, size=400)).reshape((400,1))
y = (np.random.randint(2, size=400)).reshape((40
在Logistic回归过程中,当我编写如下代码时,出现了一个错误:
logistic_regression= LogisticRegression()
logistic_regression.fit(X_train,y_train)
y_pred=logistic_regression.predict(X_test)
我有一个错误:ValueError: Input contains NaN, infinity or a value too large for dtype('float64').我应该怎么做?
我最近才开始学习数据科学。我就是这么写的:
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import KFold
from sklearn.metrics import precision_score, recall_score
import numpy as np
#reading data
df = pd.read_csv(&
计算logistic映射函数的许多代码示例
使用数组。例如
%matplotlib notebook
import numpy as np
import matplotlib.pyplot as plt
def logistic(r, x):
return r * x * (1 - x)
n = 1000
r = np.linspace(2.5, 4., n)
iterations = 1000
last = 900
x = 1e-5 * np.ones(n)
fig, ax1 = plt.subplots(figsize=(8, 8))
for i in range(it