YTHAAA的博客

深度学习------神经网络图像识别（tensorflow实现）

YTHAAA — Fri, 13 May 2022 23:01:31 +0800

本指南将训练一个神经网络模型，对运动鞋和衬衫等服装图像进行分类。即使您不理解所有细节也没关系；这只是对完整 TensorFlow 程序的快速概述，详细内容会在您实际操作的同时进行介绍。本指南使用了 tf.keras，它是 TensorFlow 中用来构建和训练模型的高级 API。

用python计算矩阵特征值和特征向量

YTHAAA — Sat, 16 Apr 2022 14:50:12 +0800

import numpy as np A = np.array([[1, -1, 3], [-5, 3, 9], [1, 0, -2]]) eigenvalue, featurevector = np.linalg.eig(A) print(eigenvalue) print(featurevector) <<< [-3.27491722 1. 4.27491722] [[ 0.43401893 0.6882472 -0.33619158] [ 0.83410767 0.6

机器学习决策树

YTHAAA — Sun, 03 Apr 2022 22:13:16 +0800

决策树(DTs)是一种用于分类和回归的非参数有监督学习方法。其目标是创建一个模型，通过学习从数据特性中推断出的简单决策规则来预测目标变量的值。决策树的一些优点： 1.易于理解和解释。树可以被可视化。 2.能够处理多输出问题。 3.使用白盒模型。如果给定的情况在模型中是可以观察到的，那么对条件的解释就很容易用布尔逻辑来解释。相反，在黑箱模型中(例如，在人工神经网络中)，结果可能很难解释。 4.可以使用统计测试验证模型。这样就有可能对模型的可靠性作出解释。 5.即使它的假设在某种程度上被生成数据的真实模型所违

使用SQL Server创建一个表

YTHAAA — Sun, 03 Apr 2022 21:32:46 +0800

CREATE TABLE website ( id int NOT NULl, name varchar(20) NOT NULL, url varchar(30), age tinyint NOT NULL, alexa int NOT NULL, PRIMARY KEY (id) );--创建一个表 sp_help website;--查看表的结构

用python matplotlib进行绘图

YTHAAA — Thu, 09 Dec 2021 23:17:57 +0800

Errorbar limit selection Illustration of selectively drawing lower and/or upper limit symbols on errorbars using the parameters , of errorbar.uplimslolims Alternatively, you can use 2xN values to draw errorbars in only one direction. Similarly and can be u

python scipy.optimize least_squares实现最小二乘法

YTHAAA — Sat, 04 Dec 2021 22:56:52 +0800

Least-squares minimization (least_squares) The code below implements least-squares estimation of and finally plots the original data and the fitted model function:X

python scipy.optimize curve_fit拟合非线性函数

YTHAAA — Wed, 01 Dec 2021 23:36:34 +0800

# -*- coding: utf-8 -*- """ Created on Wed Dec 1 23:27:21 2021 @author: Machi """ import matplotlib.pyplot as plt import numpy as np from scipy.optimize import curve_fit import seaborn as sns sns.set(color_codes=True) np.random.seed(20211201) e = np.r

用python进行时间序列分析

YTHAAA — Sat, 20 Nov 2021 11:33:06 +0800

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from statsmodels.graphics.tsaplots import plot_acf from statsmodels.tsa.stattools import adfuller as ADF from statsmodels.graphics.tsaplots import plot_pa

用python画散点图

YTHAAA — Tue, 16 Nov 2021 20:41:12 +0800

# -*- coding: utf-8 -*- """ Created on Tue Nov 16 20:38:41 2021 @author: Machi """ import numpy as np import matplotlib.pyplot as plt # Fixing random state for reproducibility %config InlineBackend.figure_format = 'retina' np.random.seed(19680801) N =

利用python，使用直方图绘制累积分布图

YTHAAA — Tue, 16 Nov 2021 20:34:34 +0800

import numpy as np import matplotlib.pyplot as plt %config InlineBackend.figure_format = 'retina' np.random.seed(190801) mu = 200 sigma = 25 n_bins = 50 x = np.random.normal(mu, sigma, size=100) fig, ax = plt.subplots(figsize=(8, 4)) # plot the cumulat

用python进行方差分析（简单版）

YTHAAA — Tue, 16 Nov 2021 12:52:37 +0800

from scipy.stats import f_oneway import numpy as np x = np.random.normal(10, 5, 20) y = np.random.normal(10, 5, 20) result = f_oneway(x,y) print(result) F_onewayResult(statistic=0.05215619706951725, pvalue=0.8205774635543236)

利用python的matplotlib库进行简单的绘图

YTHAAA — Mon, 15 Nov 2021 21:30:28 +0800

# -*- coding: utf-8 -*- """ Created on Mon Nov 15 21:19:53 2021 @author: Machi """ import matplotlib.pyplot as plt import numpy as np %config InlineBackend.figure_format = 'retina' x = np.linspace(0, 2, 100) # Note that even in the OO-style, we use `.py

用python进行方差分析

YTHAAA — Sun, 14 Nov 2021 12:52:15 +0800

# -*- coding: utf-8 -*- """ Created on Sun Nov 14 11:09:44 2021 @author: Machi """ import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt from statsmodels.formula.api import ols from statsmodels.stats.anova import an

用r语言进行方差分析

YTHAAA — Sun, 14 Nov 2021 00:08:56 +0800

a = rnorm(200) b = rnorm(200) table = data.frame( x = c(a), y = c(b) ) aov.manu <- aov(y ~ x, data=table) summary(aov.manu)

用python实现牛顿法，求解非线性方程的解

YTHAAA — Sat, 13 Nov 2021 10:25:10 +0800

# -*- coding: utf-8 -*- """ Created on Sat Nov 13 09:29:27 2021 @author: Machi """ import sympy as sp x = sp.symbols('x') def f(x): return x**3 - x def df_value(x,x_value): dy = sp.diff(f(x),x) y_value = float(dy.evalf(

r语言拟合MA模型，及时序图，自相关图，偏自相关图

YTHAAA — Thu, 11 Nov 2021 22:52:06 +0800

e<-rnorm(100) x1<-filter(e,filter = c(1,-0.5),method = "convolution",circular = T) plot(x1) acf(x1) pacf(x1)

用python求导

YTHAAA — Mon, 08 Nov 2021 21:41:33 +0800

# -*- coding: utf-8 -*- """ Created on Mon Nov 8 21:36:35 2021 @author: Machi """ import sympy as sp x, y, z = sp.symbols('x y z') func = z * sp.sin(2 * sp.pi ** x + x ** y / 5) func_x = sp.diff(func, x) func_y = sp.diff(func, y) func_z = sp.diff(func,

用python求cdf和分位数

YTHAAA — Fri, 05 Nov 2021 21:47:31 +0800

# -*- coding: utf-8 -*- """ Created on Fri Nov 5 21:44:19 2021 @author: Machi """ from scipy.stats import norm q = norm.cdf(0,0,1) print(q) print(norm.ppf(q,0,1)) 0.5 0.0

正态性检验（偏度和峰度）python

YTHAAA — Mon, 01 Nov 2021 20:26:03 +0800

# -*- coding: utf-8 -*- """ Created on Mon Nov 1 20:20:05 2021 @author: 86158 """ import numpy as np from scipy.stats import normaltest v = np.random.normal(size=100) print(normaltest(v)) 结果： NormaltestResult(statistic=0.2786470828038562, pvalue=0.86

用python求方程的解

YTHAAA — Mon, 01 Nov 2021 20:23:21 +0800

# -*- coding: utf-8 -*- """ Created on Mon Nov 1 20:20:05 2021 @author: Machi """ from scipy.optimize import root from math import cos def f(x): return x + cos(x+4) myroot = root(f, 0) print(myroot.x) # 查看更多信息 print(myroot) [0.35232971] fjac: array