def read_example():
t1 = time.time()
A = sdc.ros.read_ros_images("image_test.bag")
# crop out dashboard
B = A[:, :-50, :, :]
# intensity threshold
threshold = B.mean() + .004 * B.std()
n = B.shape[0]
mask = np.empty(n, np.bool_)
for i in prange(n):
im = B[i]
mask[i] = im.mean() > threshold
C = B[mask]
D = np.empty_like(C)
for i in prange(len(C)):
D[i, :, :, 0] = gaussian_blur(C[i, :, :, 0])
D[i, :, :, 1] = gaussian_blur(C[i, :, :, 1])
D[i, :, :, 2] = gaussian_blur(C[i, :, :, 2])
# K-means model
numCenter = 4
numIter = 10
dn, dh, dw, dc = D.shape
centroids = np.random.randint(0, 255,
(numCenter, dh, dw, dc)).astype(np.uint8)
for l in range(numIter):
dist = np.array([[
sqrt(np.sum((D[i] - centroids[j])**2)) for j in range(numCenter)
] for i in range(dn)])
labels = np.array([dist[i].argmin() for i in range(dn)])
for i in range(numCenter):
mask2 = (labels == i)
num_points = np.sum(mask2)
if num_points != 0:
centroids[i] = np.sum(D[mask2], 0) / num_points
else:
centroids[i] = np.random.randint(0, 255,
(dh, dw, dc)).astype(np.uint8)
t2 = time.time()
print("Exec time: ", t2 - t1)
return centroids
def test_impl(n):
X = np.ones(n)
b = 0.5
points = np.array([-1.0, 2.0, 5.0])
N = points.shape[0]
exps = 0
for i in sdc.prange(n):
p = X[i]
d = (-(p - points)**2) / (2 * b**2)
m = np.min(d)
exps += m - np.log(b * N) + np.log(np.sum(np.exp(d - m)))
return exps
def test_impl(nsyms):
max_num_days = 100
all_res = 0.0
for i in sdc.prange(nsyms):
s_open = 20 * np.ones(max_num_days)
s_low = 28 * np.ones(max_num_days)
s_close = 19 * np.ones(max_num_days)
df = pd.DataFrame({'Open': s_open, 'Low': s_low, 'Close': s_close})
df['Stdev'] = df['Close'].rolling(window=90).std()
df['Moving Average'] = df['Close'].rolling(window=20).mean()
df['Criteria1'] = (df['Open'] - df['Low'].shift(1)) < -df['Stdev']
df['Criteria2'] = df['Open'] > df['Moving Average']
df['BUY'] = df['Criteria1'] & df['Criteria2']
df['Pct Change'] = (df['Close'] - df['Open']) / df['Open']
df['Rets'] = df['Pct Change'][df['BUY']]
all_res += df['Rets'].mean()
return all_res
def kde():
t = pq.read_table('hdfs://localhost:9016/user/etotoni/kde.parquet')
df = t.to_pandas()
X = df['points'].values
b = 0.5
points = np.array([-1.0, 2.0, 5.0])
N = points.shape[0]
n = X.shape[0]
exps = 0
t1 = time.time()
for i in prange(n):
p = X[i]
d = (-(p - points)**2) / (2 * b**2)
m = np.min(d)
exps += m - np.log(b * N) + np.log(np.sum(np.exp(d - m)))
t = time.time() - t1
print("Execution time:", t, "\nresult:", exps)
return exps
def intraday_mean_revert():
nsyms = 1000
max_num_days = 80000
all_res = 0.0
t1 = time.time()
for i in prange(nsyms):
# np.random.seed(0)
s_open = 20 * np.random.randn(max_num_days)
s_low = 18 * np.random.randn(max_num_days)
s_close = 19 * np.random.randn(max_num_days)
df = pd.DataFrame({'Open': s_open, 'Low': s_low, 'Close': s_close})
# create column to hold our 90 day rolling standard deviation
df['Stdev'] = df['Close'].rolling(window=90).std()
# create a column to hold our 20 day moving average
df['Moving Average'] = df['Close'].rolling(window=20).mean()
# create a column which holds a TRUE value if the gap down from previous day's low to next
# day's open is larger than the 90 day rolling standard deviation
df['Criteria1'] = (df['Open'] - df['Low'].shift(1)) < -df['Stdev']
# create a column which holds a TRUE value if the opening price of the stock is above the 20 day moving average
df['Criteria2'] = df['Open'] > df['Moving Average']
# create a column that holds a TRUE value if both above criteria are also TRUE
df['BUY'] = df['Criteria1'] & df['Criteria2']
# calculate daily % return series for stock
df['Pct Change'] = (df['Close'] - df['Open']) / df['Open']
# create a strategy return series by using the daily stock returns where the trade criteria above are met
df['Rets'] = df['Pct Change'][df['BUY']]
all_res += df['Rets'].mean()
print(all_res)
print("execution time:", time.time() - t1)
