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Description: jabber jabberTest.rar - Id: pasvlogin.c,v 1.10 2003/02/16 04:18:47 dpuryear Exp $ * * Just logon n users. Do not send any message traffic (2006-04-03，C-C++,45KB,6次)
miranda-im-v0.4src.zip - 基于windows API的即时通讯软件，采用jabber开放协议，可以和msn qq等互通 (2005-06-29，C-C++,2119KB,48次)-jabber jabberTest.rar - Id : pasvlogin.c, v 1.10 2003/02/16 04:18 : 47 dpuryear Exp $ * * n Just logon users. Do not Nicht d any message traffic (2006-04-03, C-C, 45KB. 6) miranda-im-v0.4src.zip-based on the windows API that When communications software, used jabber open agreement, and can exchange such as msn qq (2005-06-29, C-C, 2119KB, 48)
Platform: |
Size: 777466 |
Author: tom |
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Description: Bi-dimensional Gabor filter with DC component compensation
This version of the 2D Gabor filter is basically a bi-dimensional Gaussian function centered at origin (0,0) with variance S modulated by a complex sinusoid with polar frequency (F,W) and phase P described by the following equation:
G(x,y,S,F,W,P)=k*Gaussian(x,y,S)*(Sinusoid(x,y,F,W,P)-DC(F,S,P)),
where:
Gaussian(x,y,S)=exp(-pi*S^2*(x^2+y^2))
Sinusoid(x,y,F,W,P)=exp(j*(2*pi*F*(x*cos(W)+y*sin(W))+P)))
DC(F,S,P)=exp(-pi*(F/S)^2+j*P)
File Id: 13776 Average rating: 0.0
Size: 1 KB # of reviews: 0
Submitted: 2007-01-26 Downloads: 274
Subscribers: 0
Keywords: gabor filter
Stiven Schwanz Dias
-Bi-dimensional Gabor filter with DC compo .. compensation This version of the 2D Gabor f ilter is basically a bi-dimensional Gaussian f unction centered at origin (0, 0) with variance S modulated by a complex sinuso id with polar frequency (F, W) and phase P described by the following equati on : G (x, y, S, F, W, P) = k * Gaussian (x, y, S) * (Sinusoid (x, y, F, W, P) - DC (F, S, P)), where : Gaussian (x, y, S) = exp (-pi * S * 2 ^ (x ^ 2 y ^ 2)) Sinusoid (x, y, F, W, P) = exp (j * (2 * pi * F * (x * cos (W) y * sin (W)) P))) D C (F, S, P) = exp (-pi * (F / S) ^ 2 * P j) File Id : 13776 Average rating : 0.0 Size : # 1 KB of reviews : 0 Submitted : 2007-01-26 Downloads : 274 Subscribers : 0 Keywords : gabor filter Stiven Schwanz Dias
Platform: |
Size: 1298 |
Author: 石峰 |
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Description: 98年全国大学生数学建模竞赛B题“水灾巡视问题”，是一个推销员问题，本题有53个点，所有可能性大约为exp(53),目前没有好方法求出精确解，既然求不出精确解，我们使用模拟退火法求出一个较优解,将所有结点编号为1到53,1到53的排列就是系统的结构,结构的变化规则是:从1到53的排列中随机选取一个子排列,将其反转或将其移至另一处,能量E自然是路径总长度。具体算法描述如下：步1： 设定初始温度T，给定一个初始的巡视路线。步2 ：步3 --8循环K次步3：步 4--7循环M次步4：随机选择路线的一段步5：随机确定将选定的路线反转或移动，即两种调整方式：反转、移动。步6：计算代价D，即调整前后的总路程的长度之差步7：按照如下规则确定是否做调整：如果D<0,则调整如果D>0,则按照EXP(-D/T)的概率进行调整步8:T*0.9-->T,降温-98 National Mathematical Contest in Modeling B and that the "flood inspections", is a salesman problem, and that is 53 points, all possibilities about exp (53), there is no good way to get accurate solutions, since no exact solution for, we used simulated annealing France obtained an optimum solution to all nodes to a number of 53 to 53.1 is with the system structure, changes in the structure of the rules is : from 1-53 with a randomly selected with a son, to reverse or to move it to another, the energy E is the natural path length. The specific algorithm is described as follows : Step 1 : The initial set temperature T, given an initial inspection line. Step 2 : Step 3 -- 8 K cycle times Step 3 : Step 4 -- 7 M cycle times Step 4 : The random selection of routes for Step 5 : rando
Platform: |
Size: 2827 |
Author: 王冠 |
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Description: 98年全国大学生数学建模竞赛B题“水灾巡视问题”，是一个推销员问题，本题有53个点，所有可能性大约为exp(53),目前没有好方法求出精确解，既然求不出精确解，我们使用模拟退火法求出一个较优解,将所有结点编号为1到53,1到53的排列就是系统的结构,结构的变化规则是:从1到53的排列中随机选取一个子排列,将其反转或将其移至另一处,能量E自然是路径总长度。具体算法描述如下：步1： 设定初始温度T，给定一个初始的巡视路线。步2 ：步3 --8循环K次步3：步 4--7循环M次步4：随机选择路线的一段步5：随机确定将选定的路线反转或移动，即两种调整方式：反转、移动。步6：计算代价D，即调整前后的总路程的长度之差步7：按照如下规则确定是否做调整：如果D<0,则调整如果D>0,则按照EXP(-D/T)的概率进行调整步8:T*0.9-->T,降温-98 National Mathematical Contest in Modeling B and that the "flood inspections", is a salesman problem, and that is 53 points, all possibilities about exp (53), there is no good way to get accurate solutions, since no exact solution for, we used simulated annealing France obtained an optimum solution to all nodes to a number of 53 to 53.1 is with the system structure, changes in the structure of the rules is : from 1-53 with a randomly selected with a son, to reverse or to move it to another, the energy E is the natural path length. The specific algorithm is described as follows : Step 1 : The initial set temperature T, given an initial inspection line. Step 2 : Step 3-- 8 K cycle times Step 3 : Step 4-- 7 M cycle times Step 4 : The random selection of routes for Step 5 : rando
Platform: |
Size: 2048 |
Author: 王冠 |
Hits:
Description: jabber jabberTest.rar - Id: pasvlogin.c,v 1.10 2003/02/16 04:18:47 dpuryear Exp $ * * Just logon n users. Do not send any message traffic (2006-04-03，C-C++,45KB,6次)
miranda-im-v0.4src.zip - 基于windows API的即时通讯软件，采用jabber开放协议，可以和msn qq等互通 (2005-06-29，C-C++,2119KB,48次)-jabber jabberTest.rar- Id : pasvlogin.c, v 1.10 2003/02/16 04:18 : 47 dpuryear Exp $** n Just logon users. Do not Nicht d any message traffic (2006-04-03, C-C, 45KB. 6) miranda-im-v0.4src.zip-based on the windows API that When communications software, used jabber open agreement, and can exchange such as msn qq (2005-06-29, C-C, 2119KB, 48)
Platform: |
Size: 777216 |
Author: tom |
Hits:
Description: Bi-dimensional Gabor filter with DC component compensation
This version of the 2D Gabor filter is basically a bi-dimensional Gaussian function centered at origin (0,0) with variance S modulated by a complex sinusoid with polar frequency (F,W) and phase P described by the following equation:
G(x,y,S,F,W,P)=k*Gaussian(x,y,S)*(Sinusoid(x,y,F,W,P)-DC(F,S,P)),
where:
Gaussian(x,y,S)=exp(-pi*S^2*(x^2+y^2))
Sinusoid(x,y,F,W,P)=exp(j*(2*pi*F*(x*cos(W)+y*sin(W))+P)))
DC(F,S,P)=exp(-pi*(F/S)^2+j*P)
File Id: 13776 Average rating: 0.0
Size: 1 KB # of reviews: 0
Submitted: 2007-01-26 Downloads: 274
Subscribers: 0
Keywords: gabor filter
Stiven Schwanz Dias
-Bi-dimensional Gabor filter with DC compo .. compensation This version of the 2D Gabor f ilter is basically a bi-dimensional Gaussian f unction centered at origin (0, 0) with variance S modulated by a complex sinuso id with polar frequency (F, W) and phase P described by the following equati on : G (x, y, S, F, W, P) = k* Gaussian (x, y, S)* (Sinusoid (x, y, F, W, P)- DC (F, S, P)), where : Gaussian (x, y, S) = exp (-pi* S* 2 ^ (x ^ 2 y ^ 2)) Sinusoid (x, y, F, W, P) = exp (j* (2* pi* F* (x* cos (W) y* sin (W)) P))) D C (F, S, P) = exp (-pi* (F/S) ^ 2* P j) File Id : 13776 Average rating : 0.0 Size :# 1 KB of reviews : 0 Submitted : 2007-01-26 Downloads : 274 Subscribers : 0 Keywords : gabor filter Stiven Schwanz Dias
Platform: |
Size: 1024 |
Author: 石峰 |
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Description: Modelo de dos rayos de Andrea Goldsmith (figura 2.5, según expresión
2.12)
f = 0.9 frecuencia en GHz
landa = 0.3/f en m
R = -1 coeficiente de reflexión en tierra
ht = 10 altura del transmisor en m
hr = 2 altura del receptor en m
Gt = 1
Gr = 1
Pt = 1
d=10:.1:100000
phase_diff = 4*pi*ht*hr./(landa*d) aproximación
Pr = Pt*((landa/(4*pi))^2)*((abs((sqrt(Gt)./d) + (R*sqrt(Gr).*(exp(-sqrt(-1)*phase_diff)))./d)).^2)
Pr = Pt*((landa/(4*pi))^2)*(1./d).*((abs((sqrt(Gt)) + (R*sqrt(Gr).*(exp(-sqrt(-1)*phase_diff))))).^2)
figure(1), clf,
plot((d),10*log10(Pr/max(abs(Pr))))
grid
xlabel( log_1_0(d) )
ylabel( Potencia recibida (dBm) )- Modelo de dos rayos de Andrea Goldsmith (figura 2.5, según expresión
2.12)
f = 0.9 frecuencia en GHz
landa = 0.3/f en m
R = -1 coeficiente de reflexión en tierra
ht = 10 altura del transmisor en m
hr = 2 altura del receptor en m
Gt = 1
Gr = 1
Pt = 1
d=10:.1:100000
phase_diff = 4*pi*ht*hr./(landa*d) aproximación
Pr = Pt*((landa/(4*pi))^2)*((abs((sqrt(Gt)./d) + (R*sqrt(Gr).*(exp(-sqrt(-1)*phase_diff)))./d)).^2)
Pr = Pt*((landa/(4*pi))^2)*(1./d).*((abs((sqrt(Gt)) + (R*sqrt(Gr).*(exp(-sqrt(-1)*phase_diff))))).^2)
figure(1), clf,
plot((d),10*log10(Pr/max(abs(Pr))))
grid
xlabel( log_1_0(d) )
ylabel( Potencia recibida (dBm) )
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Size: 1024 |
Author: ramonmro |
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Description: 七单元天线阵DOA估计
clear clc
d=1 天线阵元的间距
lma=2 信号中心波长
q1=1*pi/4 q2=1*pi/3 q3=1*pi/6 q4=3*pi/4 四输入信号的方向
A1=[exp(-2*pi*j*d*[0:6]*cos(q1)/lma)] 求阵因子
A2=[exp(-2*pi*j*d*[0:6]*cos(q2)/lma)]
A3=[exp(-2*pi*j*d*[0:6]*cos(q3)/lma)]
A4=[exp(-2*pi*j*d*[0:6]*cos(q4)/lma)]
A=[A1,A2,A3,A4] 得出A矩阵
n=1:1900
v1=.015 四信号的频率
v2=.05
v3=.02
v4=.035
d=[1.3*cos(v1*n) 1*sin(v2-we
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Size: 2048 |
Author: ww |
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Description: 这是一个简单的FFT为无电位1 +1 +1 Ð 薛定谔方程的光束传播方法。如果该软件灵活，允许引入的术语（如果是极少数需要包括色散效应高阶导数）。例如，如果一个人渴望解决的一个方程的形式：
（四/ dz的+ Ð ^ 2/dx ^ 2 - 0.25 * Ð ^ 3/dx ^ 3）== 0 Ÿ
它可以解决使用以下代码：
Ž = linspace（0,1,512）
x = linspace（-5,5,1024）
psi0 =进出口（- x的。^ 2）
Ð = D_lateral1D（十，2）-0.25 * D_lateral1D（十，3）
字段= LinearBPM（psi0，有D，Z）
凡D_lateral1D是一个函数，提供“取代”的微分算子。
这个函数是在一个非线性的BPM，我曾对（分步，谱方法等），将提交给我，当我完成他们的文件和组织功能更复杂的设置非常初期阶段。
-This is a simple FFT based beam propagation method for potential-free 1+1+1D Schrodinger equation. The software if flexible and allows to introduce high-order derivatives in the term (very handful if is needed to include dispersion effects). For example, if one desires to solve an equation in the form:
(d/dz+ d^2/dx^2- 0.25*d^3/dx^3)Y == 0
It can be solved using the following code:
z=linspace(0,1,512)
x=linspace(-5,5,1024)
psi0=exp(-x.^2)
D=D_lateral1D(x,2)-0.25*D_lateral1D(x,3)
field=LinearBPM(psi0,D,z)
Where D_lateral1D is a provided function that "replaces" the derivative operator.
This function is a very early stage in a more complex set of non-linear BPM functions that I have made (split-step, spectral methods, etc.) and I will submit them when I finish their documentation and organization.
Sorry for my English, I m eager for your comments.
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Size: 2048 |
Author: zhou |
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Description: -s svm类型：SVM设置类型(默认0)
0 -- C-SVC
1 --v-SVC
2 – 一类SVM
3 -- e -SVR
4 -- v-SVR
-t 核函数类型：核函数设置类型(默认2)
0 – 线性：u v
1 – 多项式：(r*u v + coef0)^degree
2 – RBF函数：exp(-r|u-v|^2)
3 –sigmoid：tanh(r*u v + coef0)
-d degree：核函数中的degree设置(针对多项式核函数)(默认3)
-g r(gama)：核函数中的gamma函数设置(针对多项式/rbf/sigmoid核函数)(默认1/ k)
-r coef0：核函数中的coef0设置(针对多项式/sigmoid核函数)((默认0)
-c cost：设置C-SVC，e -SVR和v-SVR的参数(损失函数)(默认1)
-n nu：设置v-SVC，一类SVM和v- SVR的参数(默认0.5)
-p p：设置e -SVR 中损失函数p的值(默认0.1)
-m cachesize：设置cache内存大小，以MB为单位(默认40)
-e eps：设置允许的终止判据(默认0.001)
-h shrinking：是否使用启发式，0或1(默认1)
-wi weight：设置第几类的参数C为weight*C(C-SVC中的C)(默认1)
-v n: n-fold交互检验模式，n为fold的个数，必须大于等于2--s svm_type : set type of SVM (default 0)
0-- C-SVC
1-- nu-SVC
2-- one-class SVM
3-- epsilon-SVR
4-- nu-SVR
-t kernel_type : set type of kernel function (default 2)
0-- linear: u *v
1-- polynomial: (gamma*u *v+ coef0)^degree
2-- radial basis function: exp(-gamma*|u-v|^2)
3-- sigmoid: tanh(gamma*u *v+ coef0)
4-- precomputed kernel (kernel values in training_instance_matrix)
-d degree : set degree in kernel function (default 3)
-g gamma : set gamma in kernel function (default 1/k)
-r coef0 : set coef0 in kernel function (default 0)
-c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)
-n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)
-m cachesize : set cache memory size in MB (default 100)
-e epsilon : set tolerance of termination criterion (default 0.001)
-h shrinking: whether to use the shrinking heuristics, 0 or 1 (default 1)
-b
Platform: |
Size: 17408 |
Author: little863 |
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Description: 这是中国科学技术大学的dsp实验内容，包括FFT，卷积，滤波器设计等内容，可供学习MATLAB以及深入理解dsp原理之用-This is dsp experiment of USTC, including FFT, convolution, filter design, etc., for learning MATLAB and in-depth understanding of the principle of dsp.
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Size: 1477632 |
Author: 林剑平 |
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Description: fit_ML_normal - Maximum Likelihood fit of the laplace distribution of i.i.d. samples!.
Given the samples of a laplace distribution, the PDF parameter is found
fits data to the probability of the form:
p(x) = 1/(2*b)*exp(-abs(x-u)/b)
with parameters: u,b
format: result = fit_ML_laplace( x,hAx )
input: x - vector, samples with laplace distribution to be parameterized
hAx - handle of an axis, on which the fitted distribution is plotted
if h is given empty, a figure is created.
output: result - structure with the fields
u,b - fitted parameters
CRB_b - Cram?r-Rao Bound for the estimator value
RMS - RMS error of the estimation
type - ML
- fit_ML_normal - Maximum Likelihood fit of the laplace distribution of i.i.d. samples!.
Given the samples of a laplace distribution, the PDF parameter is found
fits data to the probability of the form:
p(x) = 1/(2*b)*exp(-abs(x-u)/b)
with parameters: u,b
format: result = fit_ML_laplace( x,hAx )
input: x - vector, samples with laplace distribution to be parameterized
hAx - handle of an axis, on which the fitted distribution is plotted
if h is given empty, a figure is created.
output: result - structure with the fields
u,b - fitted parameters
CRB_b - Cram?r-Rao Bound for the estimator value
RMS - RMS error of the estimation
type - ML
Platform: |
Size: 1024 |
Author: resident e |
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Description: fit_ML_normal - Maximum Likelihood fit of the laplace distribution of i.i.d. samples!.
Given the samples of a laplace distribution, the PDF parameter is found
fits data to the probability of the form:
p(x) = 1/(2*b)*exp(-abs(x-u)/b)
with parameters: u,b
format: result = fit_ML_laplace( x,hAx )
input: x - vector, samples with laplace distribution to be parameterized
hAx - handle of an axis, on which the fitted distribution is plotted
if h is given empty, a figure is created.
output: result - structure with the fields
u,b - fitted parameters
CRB_b - Cram?r-Rao Bound for the estimator value
RMS - RMS error of the estimation
type - ML
- fit_ML_normal - Maximum Likelihood fit of the laplace distribution of i.i.d. samples!.
Given the samples of a laplace distribution, the PDF parameter is found
fits data to the probability of the form:
p(x) = 1/(2*b)*exp(-abs(x-u)/b)
with parameters: u,b
format: result = fit_ML_laplace( x,hAx )
input: x - vector, samples with laplace distribution to be parameterized
hAx - handle of an axis, on which the fitted distribution is plotted
if h is given empty, a figure is created.
output: result - structure with the fields
u,b - fitted parameters
CRB_b - Cram?r-Rao Bound for the estimator value
RMS - RMS error of the estimation
type - ML
Platform: |
Size: 1024 |
Author: resident e |
Hits:
Description: fit_ML_normal - Maximum Likelihood fit of the log-normal distribution of i.i.d. samples!.
Given the samples of a log-normal distribution, the PDF parameter is found
fits data to the probability of the form:
p(x) = sqrt(1/(2*pi))/(s*x)*exp(- (log(x-m)^2)/(2*s^2))
with parameters: m,s
format: result = fit_ML_log_normal( x,hAx )
input: x - vector, samples with log-normal distribution to be parameterized
hAx - handle of an axis, on which the fitted distribution is plotted
if h is given empty, a figure is created.
output: result - structure with the fields
m,s - fitted parameters
CRB_m,CRB_s - Cram?r-Rao Bound for the estimator value
RMS - RMS error of the estimation
type - ML - fit_ML_normal - Maximum Likelihood fit of the log-normal distribution of i.i.d. samples!.
Given the samples of a log-normal distribution, the PDF parameter is found
fits data to the probability of the form:
p(x) = sqrt(1/(2*pi))/(s*x)*exp(- (log(x-m)^2)/(2*s^2))
with parameters: m,s
format: result = fit_ML_log_normal( x,hAx )
input: x - vector, samples with log-normal distribution to be parameterized
hAx - handle of an axis, on which the fitted distribution is plotted
if h is given empty, a figure is created.
output: result - structure with the fields
m,s - fitted parameters
CRB_m,CRB_s - Cram?r-Rao Bound for the estimator value
RMS - RMS error of the estimation
type - ML
Platform: |
Size: 1024 |
Author: resident e |
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Description: fit_ML_normal - Maximum Likelihood fit of the normal distribution of i.i.d. samples!.
Given the samples of a normal distribution, the PDF parameter is found
fits data to the probability of the form:
p(r) = sqrt(1/2/pi/sig^2)*exp(-((r-u)^2)/(2*sig^2))
with parameters: u,sig^2
format: result = fit_ML_normal( x,hAx )
input: x - vector, samples with normal distribution to be parameterized
hAx - handle of an axis, on which the fitted distribution is plotted
if h is given empty, a figure is created.
output: result - structure with the fields
sig^2,u - fitted parameters
CRB_sig2,CRB_u - Cram?r-Rao Bound for the estimator value
RMS - RMS error of the estimation
type - ML - fit_ML_normal - Maximum Likelihood fit of the normal distribution of i.i.d. samples!.
Given the samples of a normal distribution, the PDF parameter is found
fits data to the probability of the form:
p(r) = sqrt(1/2/pi/sig^2)*exp(-((r-u)^2)/(2*sig^2))
with parameters: u,sig^2
format: result = fit_ML_normal( x,hAx )
input: x - vector, samples with normal distribution to be parameterized
hAx - handle of an axis, on which the fitted distribution is plotted
if h is given empty, a figure is created.
output: result - structure with the fields
sig^2,u - fitted parameters
CRB_sig2,CRB_u - Cram?r-Rao Bound for the estimator value
RMS - RMS error of the estimation
type - ML
Platform: |
Size: 1024 |
Author: resident e |
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Description: fit_ML_rayleigh - Maximum Likelihood fit of the rayleigh distribution of i.i.d. samples!.
Given the samples of a rayleigh distribution, the PDF parameter is found
fits data to the probability of the form:
p(r)=r*exp(-r^2/(2*s))/s
with parameter: s
format: result = fit_ML_rayleigh( x,hAx )
input: x - vector, samples with rayleigh distribution to be parameterized
hAx - handle of an axis, on which the fitted distribution is plotted
if h is given empty, a figure is created.
output: result - structure with the fields
s - fitted parameter
CRB - Cram?r-Rao Bound for the estimator value
RMS - RMS error of the estimation
type- ML -fit_ML_rayleigh - Maximum Likelihood fit of the rayleigh distribution of i.i.d. samples!.
Given the samples of a rayleigh distribution, the PDF parameter is found
fits data to the probability of the form:
p(r)=r*exp(-r^2/(2*s))/s
with parameter: s
format: result = fit_ML_rayleigh( x,hAx )
input: x - vector, samples with rayleigh distribution to be parameterized
hAx - handle of an axis, on which the fitted distribution is plotted
if h is given empty, a figure is created.
output: result - structure with the fields
s - fitted parameter
CRB - Cram?r-Rao Bound for the estimator value
RMS - RMS error of the estimation
type- ML
Platform: |
Size: 1024 |
Author: resident e |
Hits:
Description: Display the 3-d plot of 2-d functions using perspective projections:
cos(ux+vy), sin(ux+vy), sinc(x,y), exp(-(ux+vy)), exp(-|ux+vy|), exp(-|ux+0y|), for all x,y and different values of u & v
-Display the 3-d plot of 2-d functions using perspective projections:
cos(ux+vy), sin(ux+vy), sinc(x,y), exp(-(ux+vy)), exp(-|ux+vy|), exp(-|ux+0y|), for all x,y and different values of u & v
Platform: |
Size: 72704 |
Author: saksaurabh |
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Description:
Simple exercise that calculate the Taylor expansion of the exponential
function.
Input variables: degree N
vector of evaluation points, x
At each step plots the Taylor polynomial and compare with the real
function
function y=taylor_exp(N,x)
printf("Order of the expansion: d ", N)
size(x)
y=ones(size(x))
plot(x,y,"r-",x,exp(x),"b-")
legend("n=0,exp(x)")
for n=1:N
y+=(1/factorial(n))*(x.^n)
plot(x,y,"r-",x,exp(x),"b-")
xlabel("x")
ylabel("f(x)")
legend("approx","exp(x)")
pause
end
endfunction
-
Simple exercise that calculate the Taylor expansion of the exponential
function.
Input variables: degree N
vector of evaluation points, x
At each step plots the Taylor polynomial and compare with the real
function
function y=taylor_exp(N,x)
printf("Order of the expansion: d ", N)
size(x)
y=ones(size(x))
plot(x,y,"r-",x,exp(x),"b-")
legend("n=0,exp(x)")
for n=1:N
y+=(1/factorial(n))*(x.^n)
plot(x,y,"r-",x,exp(x),"b-")
xlabel("x")
ylabel("f(x)")
legend("approx","exp(x)")
pause
end
endfunction
Platform: |
Size: 4096 |
Author: ali |
Hits:
Description:
Simple exercise that calculate the Taylor expansion of the exponential
function.
Input variables: degree N
vector of evaluation points, x
At each step plots the Taylor polynomial and compare with the real
function
function y=taylor_exp(N,x)
printf("Order of the expansion: d ", N)
size(x)
y=ones(size(x))
plot(x,y,"r-",x,exp(x),"b-")
legend("n=0,exp(x)")
for n=1:N
y+=(1/factorial(n))*(x.^n)
plot(x,y,"r-",x,exp(x),"b-")
xlabel("x")
ylabel("f(x)")
legend("approx","exp(x)")
pause
end
endfunction
-
Simple exercise that calculate the Taylor expansion of the exponential
function.
Input variables: degree N
vector of evaluation points, x
At each step plots the Taylor polynomial and compare with the real
function
function y=taylor_exp(N,x)
printf("Order of the expansion: d ", N)
size(x)
y=ones(size(x))
plot(x,y,"r-",x,exp(x),"b-")
legend("n=0,exp(x)")
for n=1:N
y+=(1/factorial(n))*(x.^n)
plot(x,y,"r-",x,exp(x),"b-")
xlabel("x")
ylabel("f(x)")
legend("approx","exp(x)")
pause
end
endfunction
Platform: |
Size: 9216 |
Author: ali |
Hits:
Description: D & JK FLIP FLOP USING MATLAB SOFTWARE
Platform: |
Size: 437248 |
Author: rohit |
Hits: