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Hi I am almost new for fortran and struggling with it at the moment.
My question is,
I open a text file and wanna read muliple lines which have different colums.
(My goal is to read the element into an 1D array)
The last line in my code will print the start to end location.
I hope someone could help me.
IMPLICIT NONE
CHARACTER(500) :: MODELLINE
INTEGER(4) :: STARTPOINT, POINT, N, CNT, NROW
INTEGER(4) :: I, NNOD, J, K
INTEGER(4), ALLOCATABLE :: STARLIST(:), FINDCOL(:,:)
OPEN(UNIT=400, FILE='inputfile.txt', STATUS ='OLD')
CNT = 0
STARTPOINT =0
DO I = 1, 4000
READ(400,'(A)',END = 10001, ERR= 10001) MODELLINE
CNT = CNT + 1
IF (MODELLINE(1:7) == '*ELSET,') STARTPOINT = STARTPOINT+1
ENDDO
10001 REWIND(400)
ALLOCATE(STARLIST(STARTPOINT))
NROW = CNT
K = 0
POINT =0
DO I = 1, NROW
READ(400,'(A)') MODELLINE
IF (MODELLINE(1:7) == '*ELSET,') K = K+1
STARLIST(K) = I ! Location for *ELSET
IF (MODELLINE(1:19) == '*ELSET, ELSET=PART4') POINT = I
ENDDO
!WRITE(*,*) STARTPOINT, STARLIST, POINT
DO I = 1, STARTPOINT
IF (STARLIST(I) == POINT-1) THEN
K = STARLIST(I)-STARLIST(I+1)
ENDIF
ENDDO
WRITE(*,*) POINT, POINT-(K)-1
REWIND(400)
END
inputfile is like this
*ELSET, ELSET=PART1
203, 204, 205, 206, 207, 208, 209, 210, 211, 212
213, 214, 215, 216, 217, 218, 219, 220, 221, 222
223, 224, 225, 255, 256, 257, 258, 259, 260, 261
262, 263, 264, 265, 266, 267, 268, 269, 270, 271
272, 273, 274, 275, 276, 277, 278, 279, 308, 309
310, 311, 312, 313, 314, 315, 316, 317, 318, 319
320, 321, 322, 323, 324, 325, 326, 327, 328, 329
330, 331, 332, 333, 334, 335, 336, 347, 348, 349
350, 351, 352, 353, 354, 355, 356
2203, 2204, 2205, 2206, 2207, 2208, 2209, 2210, 2211, 2212
2213, 2214, 2215, 2216, 2217, 2218, 2219, 2220, 2221, 2222
2223, 2224, 2225, 2255, 2256, 2257, 2258, 2259, 2260, 2261
2262, 2263, 2264, 2265, 2266, 2267, 2268, 2269, 2270, 2271
2272, 2273, 2274, 2275, 2276, 2277, 2278, 2279, 2308, 2309
2310, 2311, 2312, 2313, 2314, 2315, 2316, 2317, 2318, 2319
2320, 2321, 2322, 2323, 2324, 2325, 2326, 2327, 2328, 2329
2330, 2331, 2332, 2333, 2334, 2335, 2336, 2347, 2348, 2349
2350, 2351, 2352, 2353, 2354, 2355, 2356
*ELSET, ELSET=PART2
226, 227, 228, 229, 337, 359, 465, 466, 467, 468
469, 470, 471, 472, 473, 474, 475, 476, 477, 478
479, 480, 481, 482, 483, 484, 485, 486, 487, 488
2226, 2227, 2228, 2229, 2337, 2359, 2465, 2466, 2467, 2468
2469, 2470, 2471, 2472, 2473, 2474, 2475, 2476, 2477, 2478
2479, 2480, 2481, 2482, 2483, 2484, 2485, 2486, 2487, 2488
*ELSET, ELSET=PART3
230, 231, 232, 233, 234, 235, 236, 237, 238, 239
240, 241, 242, 243, 244, 245, 246, 247, 248, 249
250, 251, 252, 253, 254
2230, 2231, 2232, 2233, 2234, 2235, 2236, 2237, 2238, 2239
2240, 2241, 2242, 2243, 2244, 2245, 2246, 2247, 2248, 2249
2250, 2251, 2252, 2253, 2254
*ELSET, ELSET=PART4
255, 256, 257, 258, 259, 260, 261, 262, 263, 264
265, 266, 267, 268, 269, 270, 271, 272, 273, 274
275, 276, 277, 278, 279
2255, 2256, 2257, 2258, 2259, 2260, 2261, 2262, 2263, 2264
2265, 2266, 2267, 2268, 2269, 2270, 2271, 2272, 2273, 2274
2275, 2276, 2277, 2278, 2279
*ELSET, ELSET=PART5
280, 281, 282, 283, 284, 285, 286, 287, 288, 289
290, 291, 292, 293, 294, 295, 296, 297, 298, 299
300, 301, 302, 303, 304, 305, 306, 307
2280, 2281, 2282, 2283, 2284, 2285, 2286, 2287, 2288, 2289
2290, 2291, 2292, 2293, 2294, 2295, 2296, 2297, 2298, 2299
2300, 2301, 2302, 2303, 2304, 2305, 2306, 2307
*ELSET, ELSET=PART6
338, 339, 340, 341, 342, 343, 344, 345, 346
2338, 2339, 2340, 2341, 2342, 2343, 2344, 2345, 2346
*ELSET, ELSET=PART7
357, 358, 360, 361, 362, 363, 364, 365, 366, 367
368, 369, 370, 371, 372, 373, 374, 375, 376, 377
378, 379, 380, 381, 382, 383, 384, 385, 386, 387
388, 389, 390, 393
2357, 2358, 2360, 2361, 2362, 2363, 2364, 2365, 2366, 2367
2368, 2369, 2370, 2371, 2372, 2373, 2374, 2375, 2376, 2377
2378, 2379, 2380, 2381, 2382, 2383, 2384, 2385, 2386, 2387
2388, 2389, 2390, 2393
I'm trying to create my model with several conv3d lstm cell layers:
I run the following code:
conv1, state1 = conv3d('conv1', _X, [8,112,112,1], [3,3,3], 64)
pool1 = max_pool('pool1', conv1, k=1)
conv2, state2 = conv3d('conv2', pool1, [8, 56, 56, 64], [3, 3, 3], 128)
pool2 = max_pool('pool2', conv2, k=2)
The conv3d functions:
def conv3d(myname, l_input, shape, kernel, outchan):
cell = contrib_rnn_cell.Conv3DLSTMCell(input_shape=shape, output_channels=out$
hidden = cell.zero_state(array_ops.shape(l_input)[0], dtypes.float32)
output, state = cell(l_input, hidden)
print(output.shape)
return output, state
My code runs OK for the conv1 and pool1 but for conv2 layer it shows me an error:
Traceback (most recent call last):
File "conv3dlstm.py", line 272, in <module>
run(16)
File "conv3dlstm.py", line 199, in run
biases)
File "/home/user/projects/model_conv3dlstm.py", line 47, in inference_c3d
conv2, state2 = conv3d('conv2', pool1, [8, 56, 56, 64], [3, 3, 3], 128)
File "/home/user/projects/model_conv3dlstm.py", line 32, in conv3d
output, state = cell(l_input, hidden)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/rnn_cell_impl.py", line 190, in __call__
return super(RNNCell, self).__call__(inputs, state)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/layers/base.py", line 696, in __call__
outputs = self.call(inputs, *args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/rnn/python/ops/rnn_cell.py", line 2110, in call
4 * self._output_channels, self._use_bias)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/rnn/python/ops/rnn_cell.py", line 2200, in _conv
"kernel", filter_size + [total_arg_size_depth, num_features], dtype=dtype)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 1297, in get_variable
constraint=constraint)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 1093, in get_variable
constraint=constraint)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 431, in get_variable
return custom_getter(**custom_getter_kwargs)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/rnn_cell_impl.py", line 193, in _rnn_get_variable
variable = getter(*args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 408, in _true_getter
use_resource=use_resource, constraint=constraint)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 747, in _get_single_variable
name, "".join(traceback.format_list(tb))))
ValueError: Variable conv_lstm_cell/kernel already exists, disallowed. Did you mean to set reuse=True or reuse=tf.AUTO_REUSE in VarScope? Originally defined at:
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/rnn/python/ops/rnn_cell.py", line 2200, in _conv
"kernel", filter_size + [total_arg_size_depth, num_features], dtype=dtype)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/rnn/python/ops/rnn_cell.py", line 2110, in call
4 * self._output_channels, self._use_bias)
File "/home/user/projects/model_conv3dlstm.py", line 32, in conv3d
output, state = cell(l_input, hidden)
I saw the code in run_cell.py at line 2200 which is:
kernel = vs.get_variable(
"kernel", filter_size + [total_arg_size_depth, num_features], dtype=dtype)
Which is getting variable with fixed name "kernel". If I understand it correctly, it is supposed to be unique. But it means I can't create more of Conv3DLSTMCells than one? Is it a bug or am I using it incorrectly?
I am new to tensorflow and tflearn and getting this error while training the model.
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'input_1/X' with dtype float
[[Node: input_1/X = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
Here is my code for preference.
X = np.array([i[0] for i in train]).reshape(-1, IMG_SIZE, IMG_SIZE, 1)
Y = np.array([i[1] for i in train])
test_x = np.array([i[0] for i in test]).reshape(-1, IMG_SIZE, IMG_SIZE, 1)
test_y = np.array([i[1] for i in test])
where train and test are numpy arrays having first element as image and second element as label. I am trying to fit my model by this line.
model.fit({'input': X}, {'targets': Y}, n_epoch=5, validation_set=({'input': test_x}, {'targets': test_y}), snapshot_step=500, show_metric=True, run_id=MODEL_NAME)
This is the complete error that I am getting:
InvalidArgumentError Traceback (most recent call last)
<ipython-input-34-cf830d06009d> in <module>()
----> 1 model.fit({'input': X}, {'targets': Y}, n_epoch=5, validation_set=({'input': test_x}, {'targets': test_y}), snapshot_step=500, show_metric=True, run_id=MODEL_NAME)
/usr/local/lib/python2.7/dist-packages/tflearn/models/dnn.pyc in fit(self, X_inputs, Y_targets, n_epoch, validation_set, show_metric, batch_size, shuffle, snapshot_epoch, snapshot_step, excl_trainops, validation_batch_size, run_id, callbacks)
213 excl_trainops=excl_trainops,
214 run_id=run_id,
--> 215 callbacks=callbacks)
216
217 def predict(self, X):
/usr/local/lib/python2.7/dist-packages/tflearn/helpers/trainer.pyc in fit(self, feed_dicts, n_epoch, val_feed_dicts, show_metric, snapshot_step, snapshot_epoch, shuffle_all, dprep_dict, daug_dict, excl_trainops, run_id, callbacks)
331 (bool(self.best_checkpoint_path) | snapshot_epoch),
332 snapshot_step,
--> 333 show_metric)
334
335 # Update training state
/usr/local/lib/python2.7/dist-packages/tflearn/helpers/trainer.pyc in _train(self, training_step, snapshot_epoch, snapshot_step, show_metric)
772 tflearn.is_training(True, session=self.session)
773 _, train_summ_str = self.session.run([self.train, self.summ_op],
--> 774 feed_batch)
775
776 # Retrieve loss value from summary string
/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc in run(self, fetches, feed_dict, options, run_metadata)
776 try:
777 result = self._run(None, fetches, feed_dict, options_ptr,
--> 778 run_metadata_ptr)
779 if run_metadata:
780 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc in _run(self, handle, fetches, feed_dict, options, run_metadata)
980 if final_fetches or final_targets:
981 results = self._do_run(handle, final_targets, final_fetches,
--> 982 feed_dict_string, options, run_metadata)
983 else:
984 results = []
/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
1030 if handle is None:
1031 return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
-> 1032 target_list, options, run_metadata)
1033 else:
1034 return self._do_call(_prun_fn, self._session, handle, feed_dict,
/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc in _do_call(self, fn, *args)
1050 except KeyError:
1051 pass
-> 1052 raise type(e)(node_def, op, message)
1053
1054 def _extend_graph(self):
InvalidArgumentError: You must feed a value for placeholder tensor 'input_1/X' with dtype float
[[Node: input_1/X = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
Caused by op u'input_1/X', defined at:
File "<string>", line 1, in <module>
File "/usr/lib/python2.7/dist-packages/IPython/kernel/zmq/kernelapp.py", line 469, in main
app.start()
File "/usr/lib/python2.7/dist-packages/IPython/kernel/zmq/kernelapp.py", line 459, in start
ioloop.IOLoop.instance().start()
File "/usr/lib/python2.7/dist-packages/zmq/eventloop/ioloop.py", line 162, in start
super(ZMQIOLoop, self).start()
File "/usr/lib/python2.7/dist-packages/tornado/ioloop.py", line 887, in start
handler_func(fd_obj, events)
File "/usr/lib/python2.7/dist-packages/tornado/stack_context.py", line 275, in null_wrapper
return fn(*args, **kwargs)
File "/usr/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events
self._handle_recv()
File "/usr/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv
self._run_callback(callback, msg)
File "/usr/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback
callback(*args, **kwargs)
File "/usr/lib/python2.7/dist-packages/tornado/stack_context.py", line 275, in null_wrapper
return fn(*args, **kwargs)
File "/usr/lib/python2.7/dist-packages/IPython/kernel/zmq/ipkernel.py", line 281, in dispatcher
return self.dispatch_shell(stream, msg)
File "/usr/lib/python2.7/dist-packages/IPython/kernel/zmq/ipkernel.py", line 245, in dispatch_shell
handler(stream, idents, msg)
File "/usr/lib/python2.7/dist-packages/IPython/kernel/zmq/ipkernel.py", line 389, in execute_request
shell.run_cell(code, store_history=store_history, silent=silent)
File "/usr/lib/python2.7/dist-packages/IPython/core/interactiveshell.py", line 2741, in run_cell
interactivity=interactivity, compiler=compiler)
File "/usr/lib/python2.7/dist-packages/IPython/core/interactiveshell.py", line 2827, in run_ast_nodes
if self.run_code(code):
File "/usr/lib/python2.7/dist-packages/IPython/core/interactiveshell.py", line 2883, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-14-fe1453e052a7>", line 6, in <module>
convnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 1], name='input')
File "/usr/local/lib/python2.7/dist-packages/tflearn/layers/core.py", line 81, in input_data
placeholder = tf.placeholder(shape=shape, dtype=dtype, name="X")
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/array_ops.py", line 1507, in placeholder
name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 1997, in _placeholder
name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 768, in apply_op
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2336, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1228, in __init__
self._traceback = _extract_stack()
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'input_1/X' with dtype float
[[Node: input_1/X = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
add dtype=np.float32 (or float64) to declare the type as float.
X = np.array([i[0] for i in train], dtype=np.float32 ).reshape(-1, IMG_SIZE, IMG_SIZE, 1)
Y = np.array([i[1] for i in train], dtype=np.float32 )
test_x = np.array([i[0] for i in test], dtype=np.float32 ).reshape(-1, IMG_SIZE, IMG_SIZE, 1)
test_y = np.array([i[1] for i in test], dtype=np.float32 )
How could I go about grouping a list of the form
((1156.0, 167, 78309.39), (1158.0, 168, 78322.731), (1156.0, 169, 65375.50), (34.0, 221, 64669.58), (34.0, 222, 91244.20), (35.0, 223, 90929.57), (34.0, 224, 90857.39))
into separate lists based on the first value in each nested list in Python. I want lists beginning with values that are within a few number of each other to be grouped together, so the result would look like:
((1156.0,167,78309.39), (1158.0, 168, 78322.73), (1156.0, 169, 65375.50))
((34.0, 221, 64669.58), (34.0, 222, 91244.20), (35.0, 223, 90929.57), (34.0, 224, 90857.39))
Thank you!
This should work
x = ((1156.0, 167, 78309.39), (1158.0, 168, 78322.731), (1156.0, 169, 65375.50), (34.0, 221, 64669.58), (34.0, 222, 91244.20), (35.0, 223, 90929.57), (34.0, 224, 90857.39))
max_step = 10
result = []
prev = None
for y in sorted(x):
if prev and y[0] - prev < max_step:
result[-1].append(y)
else:
result.append([y])
prev = y[0]
I have read that prime numbers are fixed and it is better to generate them at compile time rather than depending on runtime. So, i thought of using Template Metaprogramming which internally uses compile-time recursion. I am using Sieve of Eratosthenes method for generating prime numbers. Here is the code:
int prime[500]; // prime[i]==0 means i is prime
template<int n> void fillMultiple(int i)
{
prime[n*i]=1; // n is prime, mark all its multiples as non-prime
n*i<500?fillMultiple<n>(i+1):fillMultiple<499>(i);
}
template<> void fillMultiple<499>(int i)
{
// do nothing
// only used as dummy function
}
template<int n> void generatePrime()
{
prime[n]==0?fillMultiple<n>(2):generatePrime<n+1>(); //if n is prime,mark all the multiples of n as non-prime.
generatePrime<n+1>();
}
template<> void generatePrime<499>()
{
// do nothing
// only used as dummy function
}
int main()
{
prime[0]=prime[1]=1; // 0 and 1 is non-prime
generatePrime<2>();
for(int i=0;i<500;i++) // print prime numbers
if(0==prime[i]) // if prime[i]==0, then i is prime
cout<<i<<endl;
return 0;
}
When i run the code, i am getting Runtime error
I am not very much familiar with Template Metaprogramming.Please explain why am i getting runtime-error? If there is any error, it must have been shown at compile-time.
Or:
int p[] = { 2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71,
73, 79, 83, 89, 97, 101, 103, 107, 109, 113, 127, 131, 137, 139, 149, 151, 157, 163, 167, 173,
179, 181, 191, 193, 197, 199, 211, 223, 227, 229, 233, 239, 241, 251, 257, 263, 269, 271, 277, 281,
283, 293, 307, 311, 313, 317, 331, 337, 347, 349, 353, 359, 367, 373, 379, 383, 389, 397, 401, 409,
419, 421, 431, 433, 439, 443, 449, 457, 461, 463, 467, 479, 487, 491, 499, 503, 509, 521, 523, 541,
547, 557, 563, 569, 571, 577, 587, 593, 599, 601, 607, 613, 617, 619, 631, 641, 643, 647, 653, 659,
661, 673, 677, 683, 691, 701, 709, 719, 727, 733, 739, 743, 751, 757, 761, 769, 773, 787, 797, 809,
811, 821, 823, 827, 829, 839, 853, 857, 859, 863, 877, 881, 883, 887, 907, 911, 919, 929, 937, 941,
947, 953, 967, 971, 977, 983, 991, 997, 1009, 1013, 1019, 1021, 1031, 1033, 1039, 1049, 1051, 1061, 1063, 1069,
1087, 1091, 1093, 1097, 1103, 1109, 1117, 1123, 1129, 1151, 1153, 1163, 1171, 1181, 1187, 1193, 1201, 1213, 1217, 1223,
1229, 1231, 1237, 1249, 1259, 1277, 1279, 1283, 1289, 1291, 1297, 1301, 1303, 1307, 1319, 1321, 1327, 1361, 1367, 1373,
1381, 1399, 1409, 1423, 1427, 1429, 1433, 1439, 1447, 1451, 1453, 1459, 1471, 1481, 1483, 1487, 1489, 1493, 1499, 1511,
1523, 1531, 1543, 1549, 1553, 1559, 1567, 1571, 1579, 1583, 1597, 1601, 1607, 1609, 1613, 1619, 1621, 1627, 1637, 1657,
1663, 1667, 1669, 1693, 1697, 1699, 1709, 1721, 1723, 1733, 1741, 1747, 1753, 1759, 1777, 1783, 1787, 1789, 1801, 1811,
1823, 1831, 1847, 1861, 1867, 1871, 1873, 1877, 1879, 1889, 1901, 1907, 1913, 1931, 1933, 1949, 1951, 1973, 1979, 1987,
1993, 1997, 1999, 2003, 2011, 2017, 2027, 2029, 2039, 2053, 2063, 2069, 2081, 2083, 2087, 2089, 2099, 2111, 2113, 2129,
2131, 2137, 2141, 2143, 2153, 2161, 2179, 2203, 2207, 2213, 2221, 2237, 2239, 2243, 2251, 2267, 2269, 2273, 2281, 2287,
2293, 2297, 2309, 2311, 2333, 2339, 2341, 2347, 2351, 2357, 2371, 2377, 2381, 2383, 2389, 2393, 2399, 2411, 2417, 2423,
2437, 2441, 2447, 2459, 2467, 2473, 2477, 2503, 2521, 2531, 2539, 2543, 2549, 2551, 2557, 2579, 2591, 2593, 2609, 2617,
2621, 2633, 2647, 2657, 2659, 2663, 2671, 2677, 2683, 2687, 2689, 2693, 2699, 2707, 2711, 2713, 2719, 2729, 2731, 2741,
2749, 2753, 2767, 2777, 2789, 2791, 2797, 2801, 2803, 2819, 2833, 2837, 2843, 2851, 2857, 2861, 2879, 2887, 2897, 2903,
2909, 2917, 2927, 2939, 2953, 2957, 2963, 2969, 2971, 2999, 3001, 3011, 3019, 3023, 3037, 3041, 3049, 3061, 3067, 3079,
3083, 3089, 3109, 3119, 3121, 3137, 3163, 3167, 3169, 3181, 3187, 3191, 3203, 3209, 3217, 3221, 3229, 3251, 3253, 3257,
3259, 3271, 3299, 3301, 3307, 3313, 3319, 3323, 3329, 3331, 3343, 3347, 3359, 3361, 3371, 3373, 3389, 3391, 3407, 3413,
3433, 3449, 3457, 3461, 3463, 3467, 3469, 3491, 3499, 3511, 3517, 3527, 3529, 3533, 3539, 3541, 3547, 3557, 3559, 3571};
I have read that prime numbers are fixed and it is better to generate them at compile time rather than depending on runtime.
Yes that is a good idea.
Since they are fixed means that they only need to be generated once (not every time).
So, i thought of using Template Meta-programming which internally uses compile-time recursion.
No such a good idea.
There is a maxim recursion level set by the compilers (depends on the compiler 8>16 last time I checked (a while ago)). Best to write a one off program generate the values you want then use the code to generate the file you will compile in your real code (or cut/paste the numbers from Wikipedia like I did).
I am using Sieve of Eratosthenes method for generating prime numbers. Here
Unfortunately that is not a template meta-program.
It works at run time.
As already stated you can use an array of prime numbers for the first x prime numbers which is obviously faster than calculating them every time.
Although it's not an answer to your question here are some information on prime numbers which could be helpful depending of what you need:
Calculating first n prime numbers
Sieve of Eratosthenes (which you already know of)
Sieve of Atkin (optimized version of the algorithm from above)
Calculating the amount of prime numbers smaller than n:
Prime-counting function
Offset logarithmic integral
http://en.wikipedia.org/wiki/Prime_counting_function
Testing if prime or looking for a prime larger than a given number:
Rabin Test
Fermat primality test
Solovay-Strassen-Test
Special prime numbers:
Mersenne prime
Twin prime
This is just a very short extract on the topic of prime numbers. Depending on what your program has to do looking into these topics might be interesting.