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import numpy as np
print ("Loading package(s)")
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print ("Hello, world")
# THERE ARE THREE POPULAR TYPES
# 1. INTEGER
x = 3;
print ("Integer: %01d, %02d, %03d, %04d, %05d"
% (x, x, x, x, x))
# 2. FLOAT
x = 123.456;
print ("Float: %.0f, %.1f, %.2f, %1.2f, %2.2f"
% (x, x, x, x, x))
# 3. STRING
x = "Hello, world"
print ("String: [%s], [%3s], [%20s]"
% (x, x, x))
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dlmethods = ["ANN", "MLP", "CNN", "RNN", "DAE"]
for alg in dlmethods:
if alg in ["ANN", "MLP"]:
print ("We have seen %s" % (alg))
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dlmethods = ["ANN", "MLP", "CNN", "RNN", "DAE"];
for alg in dlmethods:
if alg in ["ANN", "MLP", "CNN"]:
print ("%s is a feed-forward network." % (alg))
elif alg in ["RNN"]:
print ("%s is a recurrent network." % (alg))
else:
print ("%s is an unsupervised method." % (alg))
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# Function definition looks like this
def sum(a, b):
return a+b
X = 10.
Y = 20.
# Usage
print ("%.1f + %.1f = %.1f" % (X, Y, sum(X, Y)))
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head = "Deep learning"
body = "very "
tail = "HARD."
print (head + " is " + body + tail)
# Repeat words
print (head + " is " + body*3 + tail)
print (head + " is " + body*10 + tail)
# It is used in this way
print ("\n" + "="*50)
print (" "*15 + "It is used in this way")
print ("="*50 + "\n")
# Indexing characters in the string
x = "Hello, world"
for i in range(len(x)):
print ("Index: [%02d/%02d] Char: %s"
% (i, len(x), x[i]))
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# More indexing
print ("")
idx = -2
print ("(%d)th char is %s" % (idx, x[idx]))
idxfr = 0
idxto = 8
print ("String from %d to %d is [%s]"
% (idxfr, idxto, x[idxfr:idxto]))
idxfr = 4
print ("String from %d to END is [%s]"
% (idxfr, x[idxfr:]))
x = "20160607Cloudy"
year = x[:4]
day = x[4:8]
weather = x[8:]
print ("[%s] -> [%s] + [%s] + [%s] "
% (x, year, day, weather))
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a = []
b = [1, 2, 3]
c = ["Hello", ",", "world"]
d = [1, 2, 3, "x", "y", "z"]
x = []
print (x)
x.append('a')
print (x)
x.append(123)
print (x)
x.append(["a", "b"])
print (x)
print ("Length of x is %d "
% (len(x)))
for i in range(len(x)):
print ("[%02d/%02d] %s"
% (i, len(x), x[i]))
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z = []
z.append(1)
z.append(2)
z.append(3)
z.append('Hello')
for i in range(len(z)):
print (z[i])
In [1]:
dic = dict()
dic["name"] = "Antonio"
dic["age"] = 43
dic["job"] = "Software Engineer"
print (dic)
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class Greeter:
# Constructor
def __init__(self, name):
self.name = name # Create an instance variable
# Instance method
def greet(self, loud=False):
if loud:
print ('HELLO, %s!'
% self.name.upper())
else:
print ('Hello, %s'
% self.name)
g = Greeter('Fred') # Construct an instance of the Greeter class
g.greet() # Call an instance method; prints "Hello, Fred"
g.greet(loud=True) # Call an instance method; prints "HELLO, FRED!"
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def print_np(x):
print ("Type is %s" % (type(x)))
print ("Shape is %s" % (x.shape,))
print ("Values are: \n%s" % (x))
print
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x = np.array([1, 2, 3]) # rank 1 array
print_np(x)
x[0] = 5
print_np(x)
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y = np.array([[1,2,3], [4,5,6]])
print_np(y)
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a = np.zeros((3, 2))
print_np(a)
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b = np.ones((1, 2))
print_np(b)
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c = np.eye(2, 2)
print_np(c)
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d = np.random.random((2, 2))
print_np(d)
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e = np.random.randn(1, 10)
print_np(e)
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# Create the following rank 2 array with shape (3, 4)
# [[ 1 2 3 4]
# [ 5 6 7 8]
# [ 9 10 11 12]]
a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
print_np(a)
print
# Use slicing to pull out the subarray consisting
# of the first 2 rows
# and columns 1 and 2; b is the following array
# of shape (2, 2):
# [[2 3]
# [6 7]]
b = a[:2, 1:3]
print_np(b)
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a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
print_np(a)
row_r1 = a[1, :] # Rank 1 view of the second row of a
row_r2 = a[1:2, :] # Rank 2 view of the second row of a
row_r3 = a[[1], :] # Rank 2 view of the second row of a
print_np(row_r1)
print_np(row_r2)
print_np(row_r3)
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a = np.array([[1,2], [3, 4], [5, 6]])
print_np(a)
# An example of integer array indexing.
# The returned array will have shape (3,) and
b = a[[0, 1, 2], [0, 1, 0]]
print_np(b)
# The above example of integer array indexing
# is equivalent to this:
c = np.array([a[0, 0], a[1, 1], a[2, 0]])
print_np(c)
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x = np.array([1, 2]) # Let numpy choose the datatype
y = np.array([1.0, 2.0]) # Let numpy choose the datatype
z = np.array([1, 2], dtype=np.int64) # particular datatype
print_np(x)
print_np(y)
print_np(z)
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x = np.array([[1,2],[3,4]], dtype=np.float64)
y = np.array([[5,6],[7,8]], dtype=np.float64)
# Elementwise sum; both produce the array
print (x + y)
print (np.add(x, y))
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# Elementwise difference; both produce the array
print (x - y)
print (np.subtract(x, y))
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# Elementwise product; both produce the array
print (x * y)
print (np.multiply(x, y))
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# Elementwise division; both produce the array
# [[ 0.2 0.33333333]
# [ 0.42857143 0.5 ]]
print (x / y)
print (np.divide(x, y))
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# Elementwise square root; produces the array
# [[ 1. 1.41421356]
# [ 1.73205081 2. ]]
print (np.sqrt(x))
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x = np.array([[1,2],[3,4]])
y = np.array([[5,6],[7,8]])
v = np.array([9,10])
w = np.array([11, 12])
print_np(x)
print_np(y)
print_np(v)
print_np(w)
# Inner product of vectors; both produce 219
print (v.dot(w))
print (np.dot(v, w)) # <= v * w'
# Matrix / vector product; both produce the rank 1 array [29 67]
print (x.dot(v))
print (np.dot(x, v)) # <= x * v'
# Matrix / matrix product; both produce the rank 2 array
# [[19 22]
# [43 50]]
print (x.dot(y))
print (np.dot(x, y))
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x = np.array([[1,2],[3,4]])
print_np(x)
print
print (x)
print (x.T)
print (np.sum(x)) # Compute sum of all elements
print (np.sum(x, axis=0)) # Compute sum of each column
print (np.sum(x, axis=1)) # Compute sum of each row
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print (x)
print (x.T)
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v = np.array([1,2,3])
print (v)
print (v.T)
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v = np.array([[1,2,3]])
print (v)
print (v.T)
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# We will add the vector v to each row of the matrix x,
# storing the result in the matrix y
x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])
v = np.array([1, 0, 1])
y = np.empty_like(x) # Create an empty matrix
# with the same shape as x
print_np(x)
print_np(v)
print_np(y)
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# Add the vector v to each row of the matrix x
# with an explicit loop
for i in range(4):
y[i, :] = x[i, :] + v
print_np(y)
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vv = np.tile(v, (4, 1)) # Stack 4 copies of v on top of each other
print_np(vv) # Prints "[[1 0 1]
# [1 0 1]
# [1 0 1]
# [1 0 1]]"
In [49]:
# We will add the vector v to each row of the matrix x,
# storing the result in the matrix y
x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])
v = np.array([1, 0, 1])
y = x + v # Add v to each row of x using BROADCASTING
print_np(x)
print_np(v)
print_np(y)
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# Add a vector to each row of a matrix
x = np.array([[1,2,3], [4,5,6]])
print_np(x)
print_np(v)
print (x + v)
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# Add a vector to each column of a matrix
print_np(x)
print_np(w)
print ((x.T + w).T)
# Another solution is to reshape w
# to be a row vector of shape (2, 1);
print
print (x + np.reshape(w, (2, 1)))
In [55]:
import matplotlib.pyplot as plt
%matplotlib inline
In [56]:
# Compute the x and y coordinates for points on a sine curve
x = np.arange(0, 3 * np.pi, 0.1)
y = np.sin(x)
# Plot the points using matplotlib
lt.plot(x, y)
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In [58]:
y_sin = np.sin(x)
y_cos = np.cos(x)
# Plot the points using matplotlib
plt.plot(x, y_sin)
plt.plot(x, y_cos)
plt.xlabel('x axis label')
plt.ylabel('y axis label')
plt.title('Sine and Cosine')
plt.legend(['Sine', 'Cosine'])
# Show the figure.
plt.show()
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