各图形的画图

1.绘制正态分布概率密度函数

mpl.rcParams['font.sans-serif'] = [u'SimHei']  #FangSong/黑体 FangSong/KaiTimpl.rcParams['axes.unicode_minus'] = Falsemu = 0sigma = 1x = np.linspace(mu - 3 * sigma, mu + 3 * sigma, 51)y = np.exp(-(x - mu) ** 2 / (2 * sigma ** 2)) / (math.sqrt(2 * math.pi) * sigma)print(x.shape)print('x = \n', x)print(y.shape)print('y = \n', y)plt.figure(facecolor='w')plt.plot(x, y, 'ro-', linewidth=2)# plt.plot(x, y, 'r-', x, y, 'go', linewidth=2, markersize=8)plt.xlabel('X', fontsize=15)plt.ylabel('Y', fontsize=15)plt.title(u'高斯分布函数', fontsize=18)    #plt.grid(True)plt.show()

2.损失函数：Logistic损失(-1,1)/SVM Hinge损失/ 0/1损失

    plt.figure(figsize=(10,8))x = np.linspace(start=-2, stop=3, num=1001, dtype=np.float)y_logit = np.log(1 + np.exp(-x)) / math.log(2)y_boost = np.exp(-x)y_01 = x < 0y_hinge = 1.0 - xy_hinge[y_hinge < 0] = 0plt.plot(x, y_logit, 'r-', label='Logistic Loss', linewidth=2)plt.plot(x, y_01, 'g-', label='0/1 Loss', linewidth=2)plt.plot(x, y_hinge, 'b-', label='Hinge Loss', linewidth=2)plt.plot(x, y_boost, 'm--', label='Adaboost Loss', linewidth=2)plt.grid()plt.legend(loc='upper right')plt.savefig('1.png')plt.show()

3.x^x

 plt.figure(facecolor='w')x = np.linspace(-1.3, 1.3, 101)y = f(x)plt.plot(x, y, 'g-', label='x^x', linewidth=2)plt.grid()plt.legend(loc='upper left')plt.show()

4.胸型线

    x = np.arange(1, 0, -0.001)y = (-3 * x * np.log(x) + np.exp(-(40 * (x - 1 / np.e)) ** 4) / 25) / 2plt.figure(figsize=(5,7), facecolor='w')plt.plot(y, x, 'r-', linewidth=2)plt.grid(True)plt.title(u'胸型线', fontsize=20)# plt.savefig('breast.png')plt.show()

5.心形线

    t = np.linspace(0, 2*np.pi, 100)x = 16 * np.sin(t) ** 3y = 13 * np.cos(t) - 5 * np.cos(2*t) - 2 * np.cos(3*t) - np.cos(4*t)plt.plot(x, y, 'r-', linewidth=2)plt.grid(True)plt.show()

6.渐开线

    t = np.linspace(0, 50, num=1000)x = t*np.sin(t) + np.cos(t)y = np.sin(t) - t*np.cos(t)plt.plot(x, y, 'r-', linewidth=2)plt.grid()plt.show()

7.Bar

    x = np.arange(0, 10, 0.1)y = np.sin(x)plt.bar(x, y, width=0.04, linewidth=0.2)plt.plot(x, y, 'r--', linewidth=2)plt.title(u'Sin曲线')plt.xticks(rotation=-60)plt.xlabel('X')plt.ylabel('Y')plt.grid()plt.show()

8.均匀分布

 x = np.random.rand(10000)t = np.arange(len(x))# plt.hist(x, 30, color='m', alpha=0.5, label=u'均匀分布')plt.plot(t, x, 'g.', label=u'均匀分布')plt.legend(loc='upper left')plt.grid()plt.show()

9.验证中心极限定理

    t = 1000a = np.zeros(10000)for i in range(t):a += np.random.uniform(-5, 5, 10000)a /= tplt.hist(a, bins=30, color='g', alpha=0.5, normed=True, label=u'均匀分布叠加')plt.legend(loc='upper left')plt.grid()plt.show()

10.其他分布的中心极限定理

 lamda = 7p = stats.poisson(lamda)y = p.rvs(size=1000)mx = 30r = (0, mx)bins = r[1] - r[0]plt.figure(figsize=(15, 8), facecolor='w')plt.subplot(121)plt.hist(y, bins=bins, range=r, color='g', alpha=0.8, normed=True)t = np.arange(0, mx+1)plt.plot(t, p.pmf(t), 'ro-', lw=2)plt.grid(True)N = 1000M = 10000plt.subplot(122)a = np.zeros(M, dtype=np.float)p = stats.poisson(lamda)for i in np.arange(N):a += p.rvs(size=M)a /= Nplt.hist(a, bins=20, color='g', alpha=0.8, normed=True)plt.grid(b=True)plt.show()

11.Poisson分布

    x = np.random.poisson(lam=5, size=10000)print(x)pillar = 15a = plt.hist(x, bins=pillar, normed=True, range=[0, pillar], color='g', alpha=0.5)plt.grid()plt.show()print(a)print(a[0].sum())

12.直方图的使用

    mu = 2sigma = 3data = mu + sigma * np.random.randn(1000)h = plt.hist(data, 30, normed=1, color='#FFFFA0')x = h[1]y = norm.pdf(x, loc=mu, scale=sigma)plt.plot(x, y, 'r-', x, y, 'ro', linewidth=2, markersize=4)plt.grid()plt.show()

13.插值

    rv = poisson(5)x1 = a[1]y1 = rv.pmf(x1)itp = BarycentricInterpolator(x1, y1)  # 重心插值x2 = np.linspace(x.min(), x.max(), 50)y2 = itp(x2)cs = sp.interpolate.CubicSpline(x1, y1)       # 三次样条插值plt.plot(x2, cs(x2), 'm--', linewidth=5, label='CubicSpine')           # 三次样条插值plt.plot(x2, y2, 'g-', linewidth=3, label='BarycentricInterpolator')   # 重心插值plt.plot(x1, y1, 'r-', linewidth=1, label='Actural Value')             # 原始值plt.legend(loc='upper right')plt.grid()plt.show()

14.Poisson分布

    size = 1000lamda = 5p = np.random.poisson(lam=lamda, size=size)plt.figure()plt.hist(p, bins=range(3 * lamda), histtype='bar', align='left', color='r', rwidth=0.8, normed=True)plt.grid(b=True, ls=':')# plt.xticks(range(0, 15, 2))plt.title('Numpy.random.poisson', fontsize=13)plt.figure()r = stats.poisson(mu=lamda)p = r.rvs(size=size)plt.hist(p, bins=range(3 * lamda), color='r', align='left', rwidth=0.8, normed=True)plt.grid(b=True, ls=':')plt.title('scipy.stats.poisson', fontsize=13)plt.show()

15.绘制三维图像

    x, y = np.mgrid[-3:3:7j, -3:3:7j]print(x)print(y)u = np.linspace(-3, 3, 101)x, y = np.meshgrid(u, u)print(x)print(y)z = x*y*np.exp(-(x**2 + y**2)/2) / math.sqrt(2*math.pi)# z = x*y*np.exp(-(x**2 + y**2)/2) / math.sqrt(2*math.pi)fig = plt.figure()ax = fig.add_subplot(111, projection='3d')# ax.plot_surface(x, y, z, rstride=5, cstride=5, cmap=cm.coolwarm, linewidth=0.1)  #ax.plot_surface(x, y, z, rstride=3, cstride=3, cmap=cm.gist_heat, linewidth=0.5)plt.show()# cmaps = [('Perceptually Uniform Sequential',#           ['viridis', 'inferno', 'plasma', 'magma']),#          ('Sequential', ['Blues', 'BuGn', 'BuPu',#                          'GnBu', 'Greens', 'Greys', 'Oranges', 'OrRd',#                          'PuBu', 'PuBuGn', 'PuRd', 'Purples', 'RdPu',#                          'Reds', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd']),#          ('Sequential (2)', ['afmhot', 'autumn', 'bone', 'cool',#                              'copper', 'gist_heat', 'gray', 'hot',#                              'pink', 'spring', 'summer', 'winter']),#          ('Diverging', ['BrBG', 'bwr', 'coolwarm', 'PiYG', 'PRGn', 'PuOr',#                         'RdBu', 'RdGy', 'RdYlBu', 'RdYlGn', 'Spectral',#                         'seismic']),#          ('Qualitative', ['Accent', 'Dark2', 'Paired', 'Pastel1',#                           'Pastel2', 'Set1', 'Set2', 'Set3']),#          ('Miscellaneous', ['gist_earth', 'terrain', 'ocean', 'gist_stern',#                             'brg', 'CMRmap', 'cubehelix',#                             'gnuplot', 'gnuplot2', 'gist_ncar',#                             'nipy_spectral', 'jet', 'rainbow',#                             'gist_rainbow', 'hsv', 'flag', 'prism'])]

16.scripy线性回归1

 x = np.linspace(-2, 2, 50)A, B, C = 2, 3, -1y = (A * x ** 2 + B * x + C) + np.random.rand(len(x))*0.75t = leastsq(residual, [0, 0, 0], args=(x, y))theta = t[0]print('真实值：', A, B, C)print('预测值：', theta)y_hat = theta[0] * x ** 2 + theta[1] * x + theta[2]plt.plot(x, y, 'r-', linewidth=2, label=u'Actual')plt.plot(x, y_hat, 'g-', linewidth=2, label=u'Predict')plt.legend(loc='upper left')plt.grid()plt.show()

17.scripy线性回归例2

 x = np.linspace(0, 5, 100)a = 5w = 1.5phi = -2y = a * np.sin(w*x) + phi + np.random.rand(len(x))*0.5t = leastsq(residual2, [3, 5, 1], args=(x, y))theta = t[0]print('真实值：', a, w, phi)print('预测值：', theta)y_hat = theta[0] * np.sin(theta[1] * x) + theta[2]plt.plot(x, y, 'r-', linewidth=2, label='Actual')plt.plot(x, y_hat, 'g-', linewidth=2, label='Predict')plt.legend(loc='lower left')plt.grid()plt.show()

18.使用scipy计算函数极值

 # a = opt.fmin(f, 1)# b = opt.fmin_cg(f, 1)# c = opt.fmin_bfgs(f, 1)# print(a, 1/a, math.e)# print(b)# print(c)# marker    description# ”.”   point# ”,”   pixel# “o”   circle# “v”   triangle_down# “^”   triangle_up# “<”   triangle_left# “>”   triangle_right# “1”   tri_down# “2”   tri_up# “3”   tri_left# “4”   tri_right# “8”   octagon# “s”   square# “p”   pentagon# “*”   star# “h”   hexagon1# “H”   hexagon2# “+”   plus# “x”   x# “D”   diamond# “d”   thin_diamond# “|”   vline# “_”   hline# TICKLEFT  tickleft# TICKRIGHT tickright# TICKUP    tickup# TICKDOWN  tickdown# CARETLEFT caretleft# CARETRIGHT    caretright# CARETUP   caretup# CARETDOWN caretdown

本文来自互联网用户投稿，文章观点仅代表作者本人，不代表本站立场，不承担相关法律责任。如若转载，请注明出处。 如若内容造成侵权/违法违规/事实不符，请点击【内容举报】进行投诉反馈！