def almost_linear(m,n): def noise(arr,n): to_insert = np.random.uniform(0,200,n) indexes = np.random.permutation(np.random.random_integers(0,299,n)) for i in range(n): arr[indexes[i]] = to_insert[i] return arr arr = np.array([m*i + n for i in range(300)]) return noise(arr,10)
def almost_linear(m, n):
def noise(arr, n):
to_insert = np.random.uniform(0, 200, n)
indexes = np.random.permutation(np.random.random_integers(0, 299, n))
for i in range(n):
arr[indexes[i]] = to_insert[i]
return arr
arr = np.array([m * i + n for i in range(300)])
return noise(arr, 10)
def zig_zag(n): arr = [100] for i in range(299): arr.append(arr[-1] + r_int(-n,n)) return np.array(arr)
def almost_linear(m,n): def noise(arr,n): to_insert = np.random.uniform(0,200,n) indexes = np.random.permutation(np.random.random_integers(0,299,n)) for i in range(n): arr[indexes[i]] = to_insert[i] return arr arr = np.array([m*i + n for i in range(300)]) return noise(arr,10) if __name__ == "__main__": #~ for i in range(10): #~ Datasets for testing #~----------------------------------------------------------------- #~ LX = zig_zag(10) #~ LX = almost_linear(2,10) #~Plots #~----------------------------------------------------------------- #~ plot_array(LX,'Stability='+str(stability(LX))) #~ plot_array(LX,'Recent Performace='+str(recent_performance(LX))) #~ plot_array(LX,'Growth='+str(growth(LX))) #~ plot_array(LX,'Score='+str(GetScore(LX))) LX = np.array([100, 100.234, 101.24, 99.3837, 103.347, 104.3864, 104.45, 105.34, 106.237, 102.348, 105.343, 107.34, 106.321, 108.486, 109.239], dtype = "float32") print str(GetScore(LX)) #~ print(str(GetScore(LX))) #~ plot_array(LX,'Score='+str(GetScore(LX)))
def zig_zag(n):
arr = [100]
for i in range(299):
arr.append(arr[-1] + r_int(-n, n))
return np.array(arr)
to_insert = np.random.uniform(0, 200, n)
indexes = np.random.permutation(np.random.random_integers(0, 299, n))
for i in range(n):
arr[indexes[i]] = to_insert[i]
return arr
arr = np.array([m * i + n for i in range(300)])
return noise(arr, 10)
if __name__ == "__main__":
#~ for i in range(10):
#~ Datasets for testing
#~-----------------------------------------------------------------
#~ LX = zig_zag(10)
#~ LX = almost_linear(2,10)
#~Plots
#~-----------------------------------------------------------------
#~ plot_array(LX,'Stability='+str(stability(LX)))
#~ plot_array(LX,'Recent Performace='+str(recent_performance(LX)))
#~ plot_array(LX,'Growth='+str(growth(LX)))
#~ plot_array(LX,'Score='+str(GetScore(LX)))
LX = np.array([
100, 100.234, 101.24, 99.3837, 103.347, 104.3864, 104.45, 105.34,
106.237, 102.348, 105.343, 107.34, 106.321, 108.486, 109.239
],
dtype="float32")
print str(GetScore(LX))
#~ print(str(GetScore(LX)))
#~ plot_array(LX,'Score='+str(GetScore(LX)))
