python basic programs

1# Program to check if a number is prime or not
2
3num = 407
4
5# To take input from the user
6#num = int(input("Enter a number: "))
7
8# prime numbers are greater than 1
9if num > 1:
10   # check for factors
11   for i in range(2,num):
12       if (num % i) == 0:
13           print(num,"is not a prime number")
14           print(i,"times",num//i,"is",num)
15           break
16   else:
17       print(num,"is a prime number")
18       
19# if input number is less than
20# or equal to 1, it is not prime
21else:
22   print(num,"is not a prime number")
23

1<pre id="3346" class="graf graf--pre graf-after--p">%matplotlib inline
2import numpy as np
3import matplotlib.pyplot as plt
4fig = plt.figure()
5ax = plt.axes(projection=’3d’)</pre>
6

1theta = 2 * np.pi * np.random.random(1000)
2r = 6 * np.random.random(1000)
3x = np.ravel(r * np.sin(theta))
4y = np.ravel(r * np.cos(theta))
5z = f(x, y)
6ax = plt.axes(projection=’3d’)
7ax.plot_trisurf(x, y, z,cmap=’viridis’, edgecolor=’none’);
8

1import numpy as np
2 
3import tensorflow as tf
4 
5 
6 
7from include.data import get_data_set
8 
9from include.model import model
10 
11 
12 
13 
14 
15test_x, test_y = get_data_set("test")
16 
17x, y, output, y_pred_cls, global_step, learning_rate = model()
18 
19 
20 
21 
22 
23_BATCH_SIZE = 128
24 
25_CLASS_SIZE = 10
26 
27_SAVE_PATH = "./tensorboard/cifar-10-v1.0.0/"
28 
29 
30 
31 
32 
33saver = tf.train.Saver()
34 
35sess = tf.Session()
36 
37 
38 
39 
40 
41try:
42 
43    print("
44Trying to restore last checkpoint ...")
45 
46    last_chk_path = tf.train.latest_checkpoint(checkpoint_dir=_SAVE_PATH)
47 
48    saver.restore(sess, save_path=last_chk_path)
49 
50    print("Restored checkpoint from:", last_chk_path)
51 
52except ValueError:
53 
54    print("
55Failed to restore checkpoint. Initializing variables instead.")
56 
57    sess.run(tf.global_variables_initializer())
58 
59 
60 
61 
62 
63def main():
64 
65    i = 0
66 
67    predicted_class = np.zeros(shape=len(test_x), dtype=np.int)
68 
69    while i < len(test_x):
70 
71        j = min(i + _BATCH_SIZE, len(test_x))
72 
73        batch_xs = test_x[i:j, :]
74 
75        batch_ys = test_y[i:j, :]
76 
77        predicted_class[i:j] = sess.run(y_pred_cls, feed_dict={x: batch_xs, y: batch_ys})
78 
79        i = j
80 
81 
82 
83    correct = (np.argmax(test_y, axis=1) == predicted_class)
84 
85    acc = correct.mean() * 100
86 
87    correct_numbers = correct.sum()
88 
89    print()
90 
91    print("Accuracy on Test-Set: {0:.2f}% ({1} / {2})".format(acc, correct_numbers, len(test_x)))
92 
93if __name__ == "__main__":
94 
95    main()
96 
97sess.close()
98

1ax = plt.axes(projection=’3d’)# Data for a three-dimensional line
2zline = np.linspace(0, 15, 1000)
3xline = np.sin(zline)
4yline = np.cos(zline)
5ax.plot3D(xline, yline, zline, ‘gray’)# Data for three-dimensional scattered points
6zdata = 15 * np.random.random(100)
7xdata = np.sin(zdata) + 0.1 * np.random.randn(100)
8ydata = np.cos(zdata) + 0.1 * np.random.randn(100)
9ax.scatter3D(xdata, ydata, zdata, c=zdata, cmap=’Greens’);
10

1from mpl_toolkits import mplot3d
2