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
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importnumpyasnpNumPy Arrays, dtypes, and shapes¶
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a=np.array([1,2,3])print(a)print(a.shape)print(a.dtype)[1 2 3] (3,) int64
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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
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np.zeros(5)Out[4]:
array([0., 0., 0., 0., 0.])
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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¶
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c=b*0.5print(c)print(c.shape)print(c.dtype)[[0. 1. 2. ] [0.5 1.5 2.5]] (2, 3) float64
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d=a+cprint(d)[[1. 3. 5. ] [1.5 3.5 5.5]]
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d[0]Out[8]:
array([1., 3., 5.])
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d[0,0]Out[9]:
1.0
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d[:,0]Out[10]:
array([1. , 1.5])
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d.sum()Out[11]:
19.5
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d.mean()Out[12]:
3.25
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d.sum(axis=0)Out[13]:
array([ 2.5, 6.5, 10.5])
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d.mean(axis=1)Out[14]:
array([3. , 3.5])
Reshape and Update In-Place¶
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e=np.arange(12)print(e)[ 0 1 2 3 4 5 6 7 8 9 10 11]
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# f is a view of contents of ef=e.reshape(3,4)print(f)[[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11]]
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# Set values of e from index 5 onwards to 0e[5:]=0print(e)[0 1 2 3 4 0 0 0 0 0 0 0]
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# f is also updatedfOut[18]:
array([[0, 1, 2, 3], [4, 0, 0, 0], [0, 0, 0, 0]])
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# OWNDATA shows f does not own its dataf.flagsOut[19]:
C_CONTIGUOUS : True F_CONTIGUOUS : False OWNDATA : False WRITEABLE : True ALIGNED : True WRITEBACKIFCOPY : False UPDATEIFCOPY : False
Combine Arrays¶
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aOut[20]:
array([1, 2, 3])
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bOut[21]:
array([[0, 2, 4], [1, 3, 5]])
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dOut[22]:
array([[1. , 3. , 5. ], [1.5, 3.5, 5.5]])
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np.concatenate([a,a,a])Out[23]:
array([1, 2, 3, 1, 2, 3, 1, 2, 3])
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# Use broadcasting when needed to do this automaticallynp.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]])
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# In machine learning, useful to enrich or # add new/concatenate features with hstacknp.hstack([b,d])Out[25]:
array([[0. , 2. , 4. , 1. , 3. , 5. ], [1. , 3. , 5. , 1.5, 3.5, 5.5]])
Create Sample Data¶
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%matplotlib inline importpylabaspltimportseabornseaborn.set()In [27]:
# Create evenly spaced numbers over the specified intervalx=np.linspace(0,2,10)plt.plot(x,'o-');plt.show()In [28]:
# Create sample data, add some noisex=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¶
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importnumpyasnpZL=np.array([[5],[2],[-1],[3]])ZLOut[29]:
array([[ 5], [ 2], [-1], [ 3]])
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t=np.exp(ZL)tOut[30]:
array([[148.4131591 ], [ 7.3890561 ], [ 0.36787944], [ 20.08553692]])
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# sum of ti and this we do by normalizing thse entries, lets add them upnp.sum(t)Out[31]:
176.25563156586637
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AL=t/np.sum(t)ALOut[32]:
array([[0.84203357], [0.04192238], [0.00208719], [0.11395685]])
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