制作公司工作网站,怎样做好营销推广,代码编程入门先学什么,旅游网站建设水平评价Gradient Descent for Logistic Regression 1. 数据集#xff08;多变量#xff09;2. 逻辑梯度下降3. 梯度下降的实现及代码描述3.1 计算梯度3.2 梯度下降 4. 数据集#xff08;单变量#xff09;附录 导入所需的库
import copy, math
import numpy as np
%matplotlib wi… Gradient Descent for Logistic Regression 1. 数据集多变量2. 逻辑梯度下降3. 梯度下降的实现及代码描述3.1 计算梯度3.2 梯度下降 4. 数据集单变量附录 导入所需的库
import copy, math
import numpy as np
%matplotlib widget
import matplotlib.pyplot as plt
from lab_utils_common import dlc, plot_data, plt_tumor_data, sigmoid, compute_cost_logistic
from plt_quad_logistic import plt_quad_logistic, plt_prob
plt.style.use(./deeplearning.mplstyle)1. 数据集多变量
X_train np.array([[0.5, 1.5], [1,1], [1.5, 0.5], [3, 0.5], [2, 2], [1, 2.5]])
y_train np.array([0, 0, 0, 1, 1, 1])fig,ax plt.subplots(1,1,figsize(4,4))
plot_data(X_train, y_train, ax)ax.axis([0, 4, 0, 3.5])
ax.set_ylabel($x_1$, fontsize12)
ax.set_xlabel($x_0$, fontsize12)
plt.show()2. 逻辑梯度下降
梯度下降计算公式: repeat until convergence: { w j w j − α ∂ J ( w , b ) ∂ w j for j : 0..n-1 b b − α ∂ J ( w , b ) ∂ b } \begin{align*} \text{repeat until convergence:} \; \lbrace \\ \; \; \;w_j w_j - \alpha \frac{\partial J(\mathbf{w},b)}{\partial w_j} \tag{1} \; \text{for j : 0..n-1} \\ \; \; \; \; \;b b - \alpha \frac{\partial J(\mathbf{w},b)}{\partial b} \\ \rbrace \end{align*} repeat until convergence:{wjwj−α∂wj∂J(w,b)bb−α∂b∂J(w,b)}for j : 0..n-1(1)
其中对于所有的 j j j 每次迭代同时更新 w j w_j wj , ∂ J ( w , b ) ∂ w j 1 m ∑ i 0 m − 1 ( f w , b ( x ( i ) ) − y ( i ) ) x j ( i ) ∂ J ( w , b ) ∂ b 1 m ∑ i 0 m − 1 ( f w , b ( x ( i ) ) − y ( i ) ) \begin{align*} \frac{\partial J(\mathbf{w},b)}{\partial w_j} \frac{1}{m} \sum\limits_{i 0}^{m-1} (f_{\mathbf{w},b}(\mathbf{x}^{(i)}) - y^{(i)})x_{j}^{(i)} \tag{2} \\ \frac{\partial J(\mathbf{w},b)}{\partial b} \frac{1}{m} \sum\limits_{i 0}^{m-1} (f_{\mathbf{w},b}(\mathbf{x}^{(i)}) - y^{(i)}) \tag{3} \end{align*} ∂wj∂J(w,b)∂b∂J(w,b)m1i0∑m−1(fw,b(x(i))−y(i))xj(i)m1i0∑m−1(fw,b(x(i))−y(i))(2)(3)
m 是训练集样例的数量 f w , b ( x ( i ) ) f_{\mathbf{w},b}(x^{(i)}) fw,b(x(i)) 是模型预测值, y ( i ) y^{(i)} y(i) 是目标值对于逻辑回归模型 z w ⋅ x b z \mathbf{w} \cdot \mathbf{x} b zw⋅xb f w , b ( x ) g ( z ) f_{\mathbf{w},b}(x) g(z) fw,b(x)g(z) 其中 g ( z ) g(z) g(z) 是 sigmoid 函数 g ( z ) 1 1 e − z g(z) \frac{1}{1e^{-z}} g(z)1e−z1
3. 梯度下降的实现及代码描述
实现梯度下降算法需要两步
循环实现上面等式(1). 即下面的 gradient_descent当前梯度的计算等式(2, 3). 即下面的 compute_gradient_logistic
3.1 计算梯度
对于所有的 w j w_j wj 和 b b b实现等式 (2),(3) 初始化变量计算 dj_dw 和 dj_db 对每个样例 计算误差 g ( w ⋅ x ( i ) b ) − y ( i ) g(\mathbf{w} \cdot \mathbf{x}^{(i)} b) - \mathbf{y}^{(i)} g(w⋅x(i)b)−y(i)对于这个样例中的每个输入值 x j ( i ) x_{j}^{(i)} xj(i) , 误差乘以输入值 x j ( i ) x_{j}^{(i)} xj(i), 然后加到对应的 dj_dw 中. (上述等式2) 累加误差到 dj_db (上述等式3) dj_db 和 dj_dw都除以样例总数 m m m 在Numpy中 x ( i ) \mathbf{x}^{(i)} x(i) 是 X[i,:] 或者X[i] x j ( i ) x_{j}^{(i)} xj(i) 是 X[i,j]
代码描述
def compute_gradient_logistic(X, y, w, b): Computes the gradient for linear regression Args:X (ndarray (m,n): Data, m examples with n featuresy (ndarray (m,)): target valuesw (ndarray (n,)): model parameters b (scalar) : model parameterReturnsdj_dw (ndarray (n,)): The gradient of the cost w.r.t. the parameters w. dj_db (scalar) : The gradient of the cost w.r.t. the parameter b. m,n X.shapedj_dw np.zeros((n,)) #(n,)dj_db 0.for i in range(m):f_wb_i sigmoid(np.dot(X[i],w) b) #(n,)(n,)scalarerr_i f_wb_i - y[i] #scalarfor j in range(n):dj_dw[j] dj_dw[j] err_i * X[i,j] #scalardj_db dj_db err_idj_dw dj_dw/m #(n,)dj_db dj_db/m #scalarreturn dj_db, dj_dw 测试一下
X_tmp np.array([[0.5, 1.5], [1,1], [1.5, 0.5], [3, 0.5], [2, 2], [1, 2.5]])
y_tmp np.array([0, 0, 0, 1, 1, 1])
w_tmp np.array([2.,3.])
b_tmp 1.
dj_db_tmp, dj_dw_tmp compute_gradient_logistic(X_tmp, y_tmp, w_tmp, b_tmp)
print(fdj_db: {dj_db_tmp} )
print(fdj_dw: {dj_dw_tmp.tolist()} )3.2 梯度下降
实现上述公式(1)代码为
def gradient_descent(X, y, w_in, b_in, alpha, num_iters): Performs batch gradient descentArgs:X (ndarray (m,n) : Data, m examples with n featuresy (ndarray (m,)) : target valuesw_in (ndarray (n,)): Initial values of model parameters b_in (scalar) : Initial values of model parameteralpha (float) : Learning ratenum_iters (scalar) : number of iterations to run gradient descentReturns:w (ndarray (n,)) : Updated values of parametersb (scalar) : Updated value of parameter # An array to store cost J and ws at each iteration primarily for graphing laterJ_history []w copy.deepcopy(w_in) #avoid modifying global w within functionb b_infor i in range(num_iters):# Calculate the gradient and update the parametersdj_db, dj_dw compute_gradient_logistic(X, y, w, b) # Update Parameters using w, b, alpha and gradientw w - alpha * dj_dw b b - alpha * dj_db # Save cost J at each iterationif i100000: # prevent resource exhaustion J_history.append( compute_cost_logistic(X, y, w, b) )# Print cost every at intervals 10 times or as many iterations if 10if i% math.ceil(num_iters / 10) 0:print(fIteration {i:4d}: Cost {J_history[-1]} )return w, b, J_history #return final w,b and J history for graphing运行一下
w_tmp np.zeros_like(X_train[0])
b_tmp 0.
alph 0.1
iters 10000w_out, b_out, _ gradient_descent(X_train, y_train, w_tmp, b_tmp, alph, iters)
print(f\nupdated parameters: w:{w_out}, b:{b_out})梯度下降的结果可视化
fig,ax plt.subplots(1,1,figsize(5,4))
# plot the probability
plt_prob(ax, w_out, b_out)# Plot the original data
ax.set_ylabel(r$x_1$)
ax.set_xlabel(r$x_0$)
ax.axis([0, 4, 0, 3.5])
plot_data(X_train,y_train,ax)# Plot the decision boundary
x0 -b_out/w_out[1]
x1 -b_out/w_out[0]
ax.plot([0,x0],[x1,0], cdlc[dlblue], lw1)
plt.show()在上图中阴影部分表示概率 y1决策边界是概率为0.5的直线。
4. 数据集单变量
导入数据绘图可视化此时参数为 w w w, b b b
x_train np.array([0., 1, 2, 3, 4, 5])
y_train np.array([0, 0, 0, 1, 1, 1])fig,ax plt.subplots(1,1,figsize(4,3))
plt_tumor_data(x_train, y_train, ax)
plt.show()w_range np.array([-1, 7])
b_range np.array([1, -14])
quad plt_quad_logistic( x_train, y_train, w_range, b_range )附录
lab_utils_common.py 源码 lab_utils_commoncontains common routines and variable definitionsused by all the labs in this week.by contrast, specific, large plotting routines will be in separate filesand are generally imported into the week where they are used.those files will import this fileimport copy
import math
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import FancyArrowPatch
from ipywidgets import Outputnp.set_printoptions(precision2)dlc dict(dlblue #0096ff, dlorange #FF9300, dldarkred#C00000, dlmagenta#FF40FF, dlpurple#7030A0)
dlblue #0096ff; dlorange #FF9300; dldarkred#C00000; dlmagenta#FF40FF; dlpurple#7030A0
dlcolors [dlblue, dlorange, dldarkred, dlmagenta, dlpurple]
plt.style.use(./deeplearning.mplstyle)def sigmoid(z):Compute the sigmoid of zParameters----------z : array_likeA scalar or numpy array of any size.Returns-------g : array_likesigmoid(z)z np.clip( z, -500, 500 ) # protect against overflowg 1.0/(1.0np.exp(-z))return g##########################################################
# Regression Routines
##########################################################def predict_logistic(X, w, b): performs prediction return sigmoid(X w b)def predict_linear(X, w, b): performs prediction return X w bdef compute_cost_logistic(X, y, w, b, lambda_0, safeFalse):Computes cost using logistic loss, non-matrix versionArgs:X (ndarray): Shape (m,n) matrix of examples with n featuresy (ndarray): Shape (m,) target valuesw (ndarray): Shape (n,) parameters for predictionb (scalar): parameter for predictionlambda_ : (scalar, float) Controls amount of regularization, 0 no regularizationsafe : (boolean) True-selects under/overflow safe algorithmReturns:cost (scalar): costm,n X.shapecost 0.0for i in range(m):z_i np.dot(X[i],w) b #(n,)(n,) or (n,) ()if safe: #avoids overflowscost -(y[i] * z_i ) log_1pexp(z_i)else:f_wb_i sigmoid(z_i) #(n,)cost -y[i] * np.log(f_wb_i) - (1 - y[i]) * np.log(1 - f_wb_i) # scalarcost cost/mreg_cost 0if lambda_ ! 0:for j in range(n):reg_cost (w[j]**2) # scalarreg_cost (lambda_/(2*m))*reg_costreturn cost reg_costdef log_1pexp(x, maximum20): approximate log(1exp^x)https://stats.stackexchange.com/questions/475589/numerical-computation-of-cross-entropy-in-practiceArgs:x : (ndarray Shape (n,1) or (n,) inputout : (ndarray Shape matches x output ~ np.log(1exp(x))out np.zeros_like(x,dtypefloat)i x maximumni np.logical_not(i)out[i] np.log(1 np.exp(x[i]))out[ni] x[ni]return outdef compute_cost_matrix(X, y, w, b, logisticFalse, lambda_0, safeTrue):Computes the cost using using matricesArgs:X : (ndarray, Shape (m,n)) matrix of examplesy : (ndarray Shape (m,) or (m,1)) target value of each examplew : (ndarray Shape (n,) or (n,1)) Values of parameter(s) of the modelb : (scalar ) Values of parameter of the modelverbose : (Boolean) If true, print out intermediate value f_wbReturns:total_cost: (scalar) costm X.shape[0]y y.reshape(-1,1) # ensure 2Dw w.reshape(-1,1) # ensure 2Dif logistic:if safe: #safe from overflowz X w b #(m,n)(n,1)(m,1)cost -(y * z) log_1pexp(z)cost np.sum(cost)/m # (scalar)else:f sigmoid(X w b) # (m,n)(n,1) (m,1)cost (1/m)*(np.dot(-y.T, np.log(f)) - np.dot((1-y).T, np.log(1-f))) # (1,m)(m,1) (1,1)cost cost[0,0] # scalarelse:f X w b # (m,n)(n,1) (m,1)cost (1/(2*m)) * np.sum((f - y)**2) # scalarreg_cost (lambda_/(2*m)) * np.sum(w**2) # scalartotal_cost cost reg_cost # scalarreturn total_cost # scalardef compute_gradient_matrix(X, y, w, b, logisticFalse, lambda_0):Computes the gradient using matricesArgs:X : (ndarray, Shape (m,n)) matrix of examplesy : (ndarray Shape (m,) or (m,1)) target value of each examplew : (ndarray Shape (n,) or (n,1)) Values of parameters of the modelb : (scalar ) Values of parameter of the modellogistic: (boolean) linear if false, logistic if truelambda_: (float) applies regularization if non-zeroReturnsdj_dw: (array_like Shape (n,1)) The gradient of the cost w.r.t. the parameters wdj_db: (scalar) The gradient of the cost w.r.t. the parameter bm X.shape[0]y y.reshape(-1,1) # ensure 2Dw w.reshape(-1,1) # ensure 2Df_wb sigmoid( X w b ) if logistic else X w b # (m,n)(n,1) (m,1)err f_wb - y # (m,1)dj_dw (1/m) * (X.T err) # (n,m)(m,1) (n,1)dj_db (1/m) * np.sum(err) # scalardj_dw (lambda_/m) * w # regularize # (n,1)return dj_db, dj_dw # scalar, (n,1)def gradient_descent(X, y, w_in, b_in, alpha, num_iters, logisticFalse, lambda_0, verboseTrue):Performs batch gradient descent to learn theta. Updates theta by takingnum_iters gradient steps with learning rate alphaArgs:X (ndarray): Shape (m,n) matrix of examplesy (ndarray): Shape (m,) or (m,1) target value of each examplew_in (ndarray): Shape (n,) or (n,1) Initial values of parameters of the modelb_in (scalar): Initial value of parameter of the modellogistic: (boolean) linear if false, logistic if truelambda_: (float) applies regularization if non-zeroalpha (float): Learning ratenum_iters (int): number of iterations to run gradient descentReturns:w (ndarray): Shape (n,) or (n,1) Updated values of parameters; matches incoming shapeb (scalar): Updated value of parameter# An array to store cost J and ws at each iteration primarily for graphing laterJ_history []w copy.deepcopy(w_in) #avoid modifying global w within functionb b_inw w.reshape(-1,1) #prep for matrix operationsy y.reshape(-1,1)for i in range(num_iters):# Calculate the gradient and update the parametersdj_db,dj_dw compute_gradient_matrix(X, y, w, b, logistic, lambda_)# Update Parameters using w, b, alpha and gradientw w - alpha * dj_dwb b - alpha * dj_db# Save cost J at each iterationif i100000: # prevent resource exhaustionJ_history.append( compute_cost_matrix(X, y, w, b, logistic, lambda_) )# Print cost every at intervals 10 times or as many iterations if 10if i% math.ceil(num_iters / 10) 0:if verbose: print(fIteration {i:4d}: Cost {J_history[-1]} )return w.reshape(w_in.shape), b, J_history #return final w,b and J history for graphingdef zscore_normalize_features(X):computes X, zcore normalized by columnArgs:X (ndarray): Shape (m,n) input data, m examples, n featuresReturns:X_norm (ndarray): Shape (m,n) input normalized by columnmu (ndarray): Shape (n,) mean of each featuresigma (ndarray): Shape (n,) standard deviation of each feature# find the mean of each column/featuremu np.mean(X, axis0) # mu will have shape (n,)# find the standard deviation of each column/featuresigma np.std(X, axis0) # sigma will have shape (n,)# element-wise, subtract mu for that column from each example, divide by std for that columnX_norm (X - mu) / sigmareturn X_norm, mu, sigma#check our work
#from sklearn.preprocessing import scale
#scale(X_orig, axis0, with_meanTrue, with_stdTrue, copyTrue)######################################################
# Common Plotting Routines
######################################################def plot_data(X, y, ax, pos_labely1, neg_labely0, s80, locbest ): plots logistic data with two axis # Find Indices of Positive and Negative Examplespos y 1neg y 0pos pos.reshape(-1,) #work with 1D or 1D y vectorsneg neg.reshape(-1,)# Plot examplesax.scatter(X[pos, 0], X[pos, 1], markerx, ss, c red, labelpos_label)ax.scatter(X[neg, 0], X[neg, 1], markero, ss, labelneg_label, facecolorsnone, edgecolorsdlblue, lw3)ax.legend(locloc)ax.figure.canvas.toolbar_visible Falseax.figure.canvas.header_visible Falseax.figure.canvas.footer_visible Falsedef plt_tumor_data(x, y, ax): plots tumor data on one axis pos y 1neg y 0ax.scatter(x[pos], y[pos], markerx, s80, c red, labelmalignant)ax.scatter(x[neg], y[neg], markero, s100, labelbenign, facecolorsnone, edgecolorsdlblue,lw3)ax.set_ylim(-0.175,1.1)ax.set_ylabel(y)ax.set_xlabel(Tumor Size)ax.set_title(Logistic Regression on Categorical Data)ax.figure.canvas.toolbar_visible Falseax.figure.canvas.header_visible Falseax.figure.canvas.footer_visible False# Draws a threshold at 0.5
def draw_vthresh(ax,x): draws a threshold ylim ax.get_ylim()xlim ax.get_xlim()ax.fill_between([xlim[0], x], [ylim[1], ylim[1]], alpha0.2, colordlblue)ax.fill_between([x, xlim[1]], [ylim[1], ylim[1]], alpha0.2, colordldarkred)ax.annotate(z 0, xy [x,0.5], xycoordsdata,xytext[30,5],textcoordsoffset points)d FancyArrowPatch(posA(x, 0.5), posB(x3, 0.5), colordldarkred,arrowstylesimple, head_width5, head_length10, tail_width0.0,)ax.add_artist(d)ax.annotate(z 0, xy [x,0.5], xycoordsdata,xytext[-50,5],textcoordsoffset points, haleft)f FancyArrowPatch(posA(x, 0.5), posB(x-3, 0.5), colordlblue,arrowstylesimple, head_width5, head_length10, tail_width0.0,)ax.add_artist(f)plt_quad_logistic.py 源码 plt_quad_logistic.pyinteractive plot and supporting routines showing logistic regression
import time
from matplotlib import cm
import matplotlib.colors as colors
from matplotlib.gridspec import GridSpec
from matplotlib.widgets import Button
from matplotlib.patches import FancyArrowPatch
from ipywidgets import Output
from lab_utils_common import np, plt, dlc, dlcolors, sigmoid, compute_cost_matrix, gradient_descent# for debug
#output Output() # sends hidden error messages to display when using widgets
#display(output)class plt_quad_logistic: plots a quad plot showing logistic regression # pylint: disabletoo-many-instance-attributes# pylint: disabletoo-many-locals# pylint: disablemissing-function-docstring# pylint: disableattribute-defined-outside-initdef __init__(self, x_train,y_train, w_range, b_range):# setup figurefig plt.figure( figsize(10,6))fig.canvas.toolbar_visible Falsefig.canvas.header_visible Falsefig.canvas.footer_visible Falsefig.set_facecolor(#ffffff) #whitegs GridSpec(2, 2, figurefig)ax0 fig.add_subplot(gs[0, 0])ax1 fig.add_subplot(gs[0, 1])ax2 fig.add_subplot(gs[1, 0], projection3d)ax3 fig.add_subplot(gs[1,1])pos ax3.get_position().get_points() ##[[lb_x,lb_y], [rt_x, rt_y]]h 0.05 width 0.2axcalc plt.axes([pos[1,0]-width, pos[1,1]-h, width, h]) #lx,by,w,hax np.array([ax0, ax1, ax2, ax3, axcalc])self.fig figself.ax axself.x_train x_trainself.y_train y_trainself.w 0. #initial point, non-arrayself.b 0.# initialize subplotsself.dplot data_plot(ax[0], x_train, y_train, self.w, self.b)self.con_plot contour_and_surface_plot(ax[1], ax[2], x_train, y_train, w_range, b_range, self.w, self.b)self.cplot cost_plot(ax[3])# setup eventsself.cid fig.canvas.mpl_connect(button_press_event, self.click_contour)self.bcalc Button(axcalc, Run Gradient Descent \nfrom current w,b (click), colordlc[dlorange])self.bcalc.on_clicked(self.calc_logistic)# output.capture() # debugdef click_contour(self, event): called when click in contour if event.inaxes self.ax[1]: #contour plotself.w event.xdataself.b event.ydataself.cplot.re_init()self.dplot.update(self.w, self.b)self.con_plot.update_contour_wb_lines(self.w, self.b)self.con_plot.path.re_init(self.w, self.b)self.fig.canvas.draw()# output.capture() # debugdef calc_logistic(self, event): called on run gradient event for it in [1, 8,16,32,64,128,256,512,1024,2048,4096]:w, self.b, J_hist gradient_descent(self.x_train.reshape(-1,1), self.y_train.reshape(-1,1),np.array(self.w).reshape(-1,1), self.b, 0.1, it,logisticTrue, lambda_0, verboseFalse)self.w w[0,0]self.dplot.update(self.w, self.b)self.con_plot.update_contour_wb_lines(self.w, self.b)self.con_plot.path.add_path_item(self.w,self.b)self.cplot.add_cost(J_hist)time.sleep(0.3)self.fig.canvas.draw()class data_plot: handles data plot # pylint: disablemissing-function-docstring# pylint: disableattribute-defined-outside-initdef __init__(self, ax, x_train, y_train, w, b):self.ax axself.x_train x_trainself.y_train y_trainself.m x_train.shape[0]self.w wself.b bself.plt_tumor_data()self.draw_logistic_lines(firsttimeTrue)self.mk_cost_lines(firsttimeTrue)self.ax.autoscale(enableFalse) # leave plot scales the same after initial setupdef plt_tumor_data(self):x self.x_trainy self.y_trainpos y 1neg y 0self.ax.scatter(x[pos], y[pos], markerx, s80, c red, labelmalignant)self.ax.scatter(x[neg], y[neg], markero, s100, labelbenign, facecolorsnone,edgecolorsdlc[dlblue],lw3)self.ax.set_ylim(-0.175,1.1)self.ax.set_ylabel(y)self.ax.set_xlabel(Tumor Size)self.ax.set_title(Logistic Regression on Categorical Data)def update(self, w, b):self.w wself.b bself.draw_logistic_lines()self.mk_cost_lines()def draw_logistic_lines(self, firsttimeFalse):if not firsttime:self.aline[0].remove()self.bline[0].remove()self.alegend.remove()xlim self.ax.get_xlim()x_hat np.linspace(*xlim, 30)y_hat sigmoid(np.dot(x_hat.reshape(-1,1), self.w) self.b)self.aline self.ax.plot(x_hat, y_hat, colordlc[dlblue],labely sigmoid(z))f_wb np.dot(x_hat.reshape(-1,1), self.w) self.bself.bline self.ax.plot(x_hat, f_wb, colordlc[dlorange], lw1,labelfz {np.squeeze(self.w):0.2f}x({self.b:0.2f}))self.alegend self.ax.legend(locupper left)def mk_cost_lines(self, firsttimeFalse): makes vertical cost linesif not firsttime:for artist in self.cost_items:artist.remove()self.cost_items []cstr fcost (1/{self.m})*(ctot 0label cost for pointaddedbreak Falsefor p in zip(self.x_train,self.y_train):f_wb_p sigmoid(self.w*p[0]self.b)c_p compute_cost_matrix(p[0].reshape(-1,1), p[1],np.array(self.w), self.b, logisticTrue, lambda_0, safeTrue)c_p_txt c_pa self.ax.vlines(p[0], p[1],f_wb_p, lw3, colordlc[dlpurple], lsdotted, labellabel)label #just onecxy [p[0], p[1] (f_wb_p-p[1])/2]b self.ax.annotate(f{c_p_txt:0.1f}, xycxy, xycoordsdata,colordlc[dlpurple],xytext(5, 0), textcoordsoffset points)cstr f{c_p_txt:0.1f} if len(cstr) 38 and addedbreak is False:cstr \naddedbreak Truectot c_pself.cost_items.extend((a,b))ctot ctot/(len(self.x_train))cstr cstr[:-1] f) {ctot:0.2f}## todo.. figure out how to get this textbox to extend to the width of the subplotc self.ax.text(0.05,0.02,cstr, transformself.ax.transAxes, colordlc[dlpurple])self.cost_items.append(c)class contour_and_surface_plot: plots combined in class as they have similar operations # pylint: disablemissing-function-docstring# pylint: disableattribute-defined-outside-initdef __init__(self, axc, axs, x_train, y_train, w_range, b_range, w, b):self.x_train x_trainself.y_train y_trainself.axc axcself.axs axs#setup useful ranges and common linspacesb_space np.linspace(*b_range, 100)w_space np.linspace(*w_range, 100)# get cost for w,b ranges for contour and 3Dtmp_b,tmp_w np.meshgrid(b_space,w_space)z np.zeros_like(tmp_b)for i in range(tmp_w.shape[0]):for j in range(tmp_w.shape[1]):z[i,j] compute_cost_matrix(x_train.reshape(-1,1), y_train, tmp_w[i,j], tmp_b[i,j],logisticTrue, lambda_0, safeTrue)if z[i,j] 0:z[i,j] 1e-9### plot contour ###CS axc.contour(tmp_w, tmp_b, np.log(z),levels12, linewidths2, alpha0.7,colorsdlcolors)axc.set_title(log(Cost(w,b)))axc.set_xlabel(w, fontsize10)axc.set_ylabel(b, fontsize10)axc.set_xlim(w_range)axc.set_ylim(b_range)self.update_contour_wb_lines(w, b, firsttimeTrue)axc.text(0.7,0.05,Click to choose w,b, bboxdict(facecolorwhite, ec black), fontsize 10,transformaxc.transAxes, verticalalignment center, horizontalalignment center)#Surface plot of the cost function J(w,b)axs.plot_surface(tmp_w, tmp_b, z, cmap cm.jet, alpha0.3, antialiasedTrue)axs.plot_wireframe(tmp_w, tmp_b, z, colork, alpha0.1)axs.set_xlabel($w$)axs.set_ylabel($b$)axs.zaxis.set_rotate_label(False)axs.xaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))axs.yaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))axs.zaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))axs.set_zlabel(J(w, b), rotation90)axs.view_init(30, -120)axs.autoscale(enableFalse)axc.autoscale(enableFalse)self.path path(self.w,self.b, self.axc) # initialize an empty path, avoids existance checkdef update_contour_wb_lines(self, w, b, firsttimeFalse):self.w wself.b bcst compute_cost_matrix(self.x_train.reshape(-1,1), self.y_train, np.array(self.w), self.b,logisticTrue, lambda_0, safeTrue)# remove lines and re-add on contour plot and 3d plotif not firsttime:for artist in self.dyn_items:artist.remove()a self.axc.scatter(self.w, self.b, s100, colordlc[dlblue], zorder 10, labelcost with \ncurrent w,b)b self.axc.hlines(self.b, self.axc.get_xlim()[0], self.w, lw4, colordlc[dlpurple], lsdotted)c self.axc.vlines(self.w, self.axc.get_ylim()[0] ,self.b, lw4, colordlc[dlpurple], lsdotted)d self.axc.annotate(fCost: {cst:0.2f}, xy (self.w, self.b), xytext (4,4), textcoords offset points,bboxdict(facecolorwhite), size 10)#Add point in 3D surface plote self.axs.scatter3D(self.w, self.b, cst , markerX, s100)self.dyn_items [a,b,c,d,e]class cost_plot: manages cost plot for plt_quad_logistic # pylint: disablemissing-function-docstring# pylint: disableattribute-defined-outside-initdef __init__(self,ax):self.ax axself.ax.set_ylabel(log(cost))self.ax.set_xlabel(iteration)self.costs []self.cline self.ax.plot(0,0, colordlc[dlblue])def re_init(self):self.ax.clear()self.__init__(self.ax)def add_cost(self,J_hist):self.costs.extend(J_hist)self.cline[0].remove()self.cline self.ax.plot(self.costs)class path: tracks paths during gradient descent on contour plot # pylint: disablemissing-function-docstring# pylint: disableattribute-defined-outside-initdef __init__(self, w, b, ax): w, b at start of path self.path_items []self.w wself.b bself.ax axdef re_init(self, w, b):for artist in self.path_items:artist.remove()self.path_items []self.w wself.b bdef add_path_item(self, w, b):a FancyArrowPatch(posA(self.w, self.b), posB(w, b), colordlc[dlblue],arrowstylesimple, head_width5, head_length10, tail_width0.0,)self.ax.add_artist(a)self.path_items.append(a)self.w wself.b b#-----------
# related to the logistic gradient descent lab
#----------def truncate_colormap(cmap, minval0.0, maxval1.0, n100): truncates color map new_cmap colors.LinearSegmentedColormap.from_list(trunc({n},{a:.2f},{b:.2f}).format(ncmap.name, aminval, bmaxval),cmap(np.linspace(minval, maxval, n)))return new_cmapdef plt_prob(ax, w_out,b_out): plots a decision boundary but include shading to indicate the probability #setup useful ranges and common linspacesx0_space np.linspace(0, 4 , 100)x1_space np.linspace(0, 4 , 100)# get probability for x0,x1 rangestmp_x0,tmp_x1 np.meshgrid(x0_space,x1_space)z np.zeros_like(tmp_x0)for i in range(tmp_x0.shape[0]):for j in range(tmp_x1.shape[1]):z[i,j] sigmoid(np.dot(w_out, np.array([tmp_x0[i,j],tmp_x1[i,j]])) b_out)cmap plt.get_cmap(Blues)new_cmap truncate_colormap(cmap, 0.0, 0.5)pcm ax.pcolormesh(tmp_x0, tmp_x1, z,normcm.colors.Normalize(vmin0, vmax1),cmapnew_cmap, shadingnearest, alpha 0.9)ax.figure.colorbar(pcm, axax)
