scipy

In [3]:

import numpy as np
import matplotlib.pyplot as plt
from scipy import optimize

import numpy as np import matplotlib.pyplot as plt from scipy import optimize

In [11]:

x = np.linspace(0,10, 21)
x

x = np.linspace(0,10, 21) x

Out[11]:

array([ 0. ,  0.5,  1. ,  1.5,  2. ,  2.5,  3. ,  3.5,  4. ,  4.5,  5. ,
        5.5,  6. ,  6.5,  7. ,  7.5,  8. ,  8.5,  9. ,  9.5, 10. ])

In [49]:

y = 3*x+4
y

y = 3*x+4 y

Out[49]:

array([ 4. ,  5.5,  7. ,  8.5, 10. , 11.5, 13. , 14.5, 16. , 17.5, 19. ,
       20.5, 22. , 23.5, 25. , 26.5, 28. , 29.5, 31. , 32.5, 34. ])

In [73]:

fig = plt.figure()
plt.scatter(x,y)
plt.plot(x,y)

fig = plt.figure() plt.scatter(x,y) plt.plot(x,y)

Out[73]:

[<matplotlib.lines.Line2D at 0x13217768520>]

In [53]:

y1 = np.random.uniform(low=-1, high=1, size=y.shape)*3
y1 = y1 +y
y1

y1 = np.random.uniform(low=-1, high=1, size=y.shape)*3 y1 = y1 +y y1

Out[53]:

array([ 5.31833371,  5.76020449,  8.87245009,  6.3823421 ,  7.52872377,
        9.24035315, 15.9268918 , 15.97524007, 14.99819733, 15.2770018 ,
       18.33654815, 20.84173274, 24.40244943, 24.57254997, 24.25786796,
       28.44592888, 25.24881334, 28.16143712, 30.1461995 , 31.01584478,
       34.61264811])

In [54]:

plt.scatter(x,y1)

plt.scatter(x,y1)

Out[54]:

<matplotlib.collections.PathCollection at 0x1321592bfa0>

In [56]:

p, cov = optimize.curve_fit(lambda x, m, l: x*m+l, x, y)
print(p, cov)

p, cov = optimize.curve_fit(lambda x, m, l: x*m+l, x, y) print(p, cov)

[3. 4.] [[ 0. -0.]
 [-0.  0.]]

In [57]:

p, cov = optimize.curve_fit(lambda x, m, l: x*m+l, x, y1)
print(p, cov)

p, cov = optimize.curve_fit(lambda x, m, l: x*m+l, x, y1) print(p, cov)

[2.93175338 4.16607872] [[ 0.01657997 -0.08289987]
 [-0.08289987  0.56648246]]

In [92]:

y_fit = x*p[0] + p[1]

plt.figure(1, figsize=(5,5))
plt.scatter(x,y1, color="teal")
plt.plot(x,y_fit, color='orange' )

y_fit = x*p[0] + p[1] plt.figure(1, figsize=(5,5)) plt.scatter(x,y1, color="teal") plt.plot(x,y_fit, color='orange' )

Out[92]:

[<matplotlib.lines.Line2D at 0x1321929b9a0>]

In [131]:

y_fit = x*p[0] + p[1]
fig ,ax= plt.subplots( figsize=(5,10))

plt.title("Regrassão linear", fontsize=25)
plt.scatter(x,y1, color="teal", s=30,label = 'Valores observados')
plt.plot(x,y_fit, color='orange',linewidth=1, label = 'Ajuste de função' )
plt.legend(loc = 'upper left')

plt.xlabel('valores x', fontsize=18)
plt.ylabel('valores y', fontsize=18)
#ax.set_aspect('equal')
plt.grid(axis='y', color='lightgrey')
plt.grid(axis='x', color='lightgrey')

plt.savefig( 'Exemplo_reg_lin' + '.png', format ='png',  bbox_inches='tight')

y_fit = x*p[0] + p[1] fig ,ax= plt.subplots( figsize=(5,10)) plt.title("Regrassão linear", fontsize=25) plt.scatter(x,y1, color="teal", s=30,label = 'Valores observados') plt.plot(x,y_fit, color='orange',linewidth=1, label = 'Ajuste de função' ) plt.legend(loc = 'upper left') plt.xlabel('valores x', fontsize=18) plt.ylabel('valores y', fontsize=18) #ax.set_aspect('equal') plt.grid(axis='y', color='lightgrey') plt.grid(axis='x', color='lightgrey') plt.savefig( 'Exemplo_reg_lin' + '.png', format ='png', bbox_inches='tight')