Credits: Forked from Parallel Machine Learning with scikit-learn and IPython by Olivier Grisel
import numpy as np
a = np.array([1, 2, 3])
print(a)
print(a.shape)
print(a.dtype)
[1 2 3] (3,) int64
b = np.array([[0, 2, 4], [1, 3, 5]])
print(b)
print(b.shape)
print(b.dtype)
[[0 2 4] [1 3 5]] (2, 3) int64
np.zeros(5)
array([0., 0., 0., 0., 0.])
np.ones(shape=(3, 4), dtype=np.int32)
array([[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]], dtype=int32)
c = b * 0.5
print(c)
print(c.shape)
print(c.dtype)
[[0. 1. 2. ] [0.5 1.5 2.5]] (2, 3) float64
d = a + c
print(d)
[[1. 3. 5. ] [1.5 3.5 5.5]]
d[0]
array([1., 3., 5.])
d[0, 0]
1.0
d[:, 0]
array([1. , 1.5])
d.sum()
19.5
d.mean()
3.25
d.sum(axis=0)
array([ 2.5, 6.5, 10.5])
d.mean(axis=1)
array([3. , 3.5])
e = np.arange(12)
print(e)
[ 0 1 2 3 4 5 6 7 8 9 10 11]
# f is a view of contents of e
f = e.reshape(3, 4)
print(f)
[[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11]]
# Set values of e from index 5 onwards to 0
e[5:] = 0
print(e)
[0 1 2 3 4 0 0 0 0 0 0 0]
# f is also updated
f
array([[0, 1, 2, 3], [4, 0, 0, 0], [0, 0, 0, 0]])
# OWNDATA shows f does not own its data
f.flags
C_CONTIGUOUS : True F_CONTIGUOUS : False OWNDATA : False WRITEABLE : True ALIGNED : True WRITEBACKIFCOPY : False UPDATEIFCOPY : False
a
array([1, 2, 3])
b
array([[0, 2, 4], [1, 3, 5]])
d
array([[1. , 3. , 5. ], [1.5, 3.5, 5.5]])
np.concatenate([a, a, a])
array([1, 2, 3, 1, 2, 3, 1, 2, 3])
# Use broadcasting when needed to do this automatically
np.vstack([a, b, d])
array([[1. , 2. , 3. ], [0. , 2. , 4. ], [1. , 3. , 5. ], [1. , 3. , 5. ], [1.5, 3.5, 5.5]])
# In machine learning, useful to enrich or
# add new/concatenate features with hstack
np.hstack([b, d])
array([[0. , 2. , 4. , 1. , 3. , 5. ], [1. , 3. , 5. , 1.5, 3.5, 5.5]])
%matplotlib inline
import pylab as plt
import seaborn
seaborn.set()
# Create evenly spaced numbers over the specified interval
x = np.linspace(0, 2, 10)
plt.plot(x, 'o-');
plt.show()
# Create sample data, add some noise
x = np.random.uniform(1, 100, 1000)
y = np.log(x) + np.random.normal(0, .3, 1000)
plt.scatter(x, y)
plt.show()
import numpy as np
ZL = np.array([[5],[2],[-1],[3]])
ZL
array([[ 5], [ 2], [-1], [ 3]])
t = np.exp(ZL)
t
array([[148.4131591 ], [ 7.3890561 ], [ 0.36787944], [ 20.08553692]])
# sum of ti and this we do by normalizing thse entries, lets add them up
np.sum(t)
176.25563156586637
AL = t / np.sum(t)
AL
array([[0.84203357], [0.04192238], [0.00208719], [0.11395685]])