NumPy¶
Credits: Forked from Parallel Machine Learning with scikit-learn and IPython by Olivier Grisel
- NumPy Arrays, dtype, and shape
- Common Array Operations
- Reshape and Update In-Place
- Combine Arrays
- Create Sample Data
In [1]:
import numpy as np
NumPy Arrays, dtypes, and shapes¶
In [2]:
a = np.array([1, 2, 3])
print(a)
print(a.shape)
print(a.dtype)
[1 2 3] (3,) int64
In [3]:
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
In [4]:
np.zeros(5)
Out[4]:
array([0., 0., 0., 0., 0.])
In [5]:
np.ones(shape=(3, 4), dtype=np.int32)
Out[5]:
array([[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1]], dtype=int32)
Common Array Operations¶
In [6]:
c = b * 0.5
print(c)
print(c.shape)
print(c.dtype)
[[0. 1. 2. ] [0.5 1.5 2.5]] (2, 3) float64
In [7]:
d = a + c
print(d)
[[1. 3. 5. ] [1.5 3.5 5.5]]
In [8]:
d[0]
Out[8]:
array([1., 3., 5.])
In [9]:
d[0, 0]
Out[9]:
1.0
In [10]:
d[:, 0]
Out[10]:
array([1. , 1.5])
In [11]:
d.sum()
Out[11]:
19.5
In [12]:
d.mean()
Out[12]:
3.25
In [13]:
d.sum(axis=0)
Out[13]:
array([ 2.5, 6.5, 10.5])
In [14]:
d.mean(axis=1)
Out[14]:
array([3. , 3.5])
Reshape and Update In-Place¶
In [15]:
e = np.arange(12)
print(e)
[ 0 1 2 3 4 5 6 7 8 9 10 11]
In [16]:
# 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]]
In [17]:
# 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]
In [18]:
# f is also updated
f
Out[18]:
array([[0, 1, 2, 3],
[4, 0, 0, 0],
[0, 0, 0, 0]])
In [19]:
# OWNDATA shows f does not own its data
f.flags
Out[19]:
C_CONTIGUOUS : True F_CONTIGUOUS : False OWNDATA : False WRITEABLE : True ALIGNED : True WRITEBACKIFCOPY : False UPDATEIFCOPY : False
Combine Arrays¶
In [20]:
a
Out[20]:
array([1, 2, 3])
In [21]:
b
Out[21]:
array([[0, 2, 4],
[1, 3, 5]])
In [22]:
d
Out[22]:
array([[1. , 3. , 5. ],
[1.5, 3.5, 5.5]])
In [23]:
np.concatenate([a, a, a])
Out[23]:
array([1, 2, 3, 1, 2, 3, 1, 2, 3])
In [24]:
# Use broadcasting when needed to do this automatically
np.vstack([a, b, d])
Out[24]:
array([[1. , 2. , 3. ],
[0. , 2. , 4. ],
[1. , 3. , 5. ],
[1. , 3. , 5. ],
[1.5, 3.5, 5.5]])
In [25]:
# In machine learning, useful to enrich or
# add new/concatenate features with hstack
np.hstack([b, d])
Out[25]:
array([[0. , 2. , 4. , 1. , 3. , 5. ],
[1. , 3. , 5. , 1.5, 3.5, 5.5]])
Create Sample Data¶
In [26]:
%matplotlib inline
import pylab as plt
import seaborn
seaborn.set()
In [27]:
# Create evenly spaced numbers over the specified interval
x = np.linspace(0, 2, 10)
plt.plot(x, 'o-');
plt.show()
In [28]:
# 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()
Softmax Function Explanation - Super basics¶
In [29]:
import numpy as np
ZL = np.array([[5],[2],[-1],[3]])
ZL
Out[29]:
array([[ 5],
[ 2],
[-1],
[ 3]])
In [30]:
t = np.exp(ZL)
t
Out[30]:
array([[148.4131591 ],
[ 7.3890561 ],
[ 0.36787944],
[ 20.08553692]])
In [31]:
# sum of ti and this we do by normalizing thse entries, lets add them up
np.sum(t)
Out[31]:
176.25563156586637
In [32]:
AL = t / np.sum(t)
AL
Out[32]:
array([[0.84203357],
[0.04192238],
[0.00208719],
[0.11395685]])
In [ ]: