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I have trained an RL agent using DQN algorithm. After 20000 episodes my rewards are converged. Now when I test this agent, the agent is always taking the same action , irrespective of state. I find this very weird. Can someone help me with this. Is there a reason, anyone can think of why is the agent behaving this way?
Reward plot
When I test the agent
state = env.reset()
print('State: ', state)
state_encod = np.reshape(state, [1, state_size])
q_values = model.predict(state_encod)
action_key = np.argmax(q_values)
print(action_key)
print(index_to_action_mapping[action_key])
print(q_values[0][0])
print(q_values[0][action_key])
q_values_plotting = []
for i in range(0,action_size):
q_values_plotting.append(q_values[0][i])
plt.plot(np.arange(0,action_size),q_values_plotting)
Every time it gives the same q_values plot, even though state initialized is different every time.Below is the q_Value plot.
Testing:
code
test_rewards = []
for episode in range(1000):
terminal_state = False
state = env.reset()
episode_reward = 0
while terminal_state == False:
print('State: ', state)
state_encod = np.reshape(state, [1, state_size])
q_values = model.predict(state_encod)
action_key = np.argmax(q_values)
action = index_to_action_mapping[action_key]
print('Action: ', action)
next_state, reward, terminal_state = env.step(state, action)
print('Next_state: ', next_state)
print('Reward: ', reward)
print('Terminal_state: ', terminal_state, '\n')
print('----------------------------')
episode_reward += reward
state = deepcopy(next_state)
print('Episode Reward' + str(episode_reward))
test_rewards.append(episode_reward)
plt.plot(test_rewards)
Thanks.
Adding environment
import gym
import rom_vav_150mm_polyreg as rom
import numpy as np
import random
class VAVenv(gym.Env):
def __init__(self):
# Zone temperature set point and limits
self.temp_sp = 24
self.temp_sp_max = 24.5
self.temp_sp_min = 23.7
# no; of hours in an episode and time interval for each step
self.MAXSTEPS = 11
self.time_interval = 5./60. #in hrs
# constants
self.zone_volume = 775
def step(self,state,action):
# state -> Time, Volume, Load, SAT ,RAT
# action -> CFM
action_cfm = action[0]
# damper_opening = state[2]
load = state[2]
sat = state[3]
current_temp = state[4]
#input
inputs_rat = np.array([load,action_cfm, self.zone_volume,current_temp,sat])
'''
AFTER 5 MINUTES
'''
#output
output = [self.KStep + self.time_interval,self.zone_volume,rom.load(self.KStep + self.time_interval),
sat,rom.rat(inputs_rat)]
#reward calculation
thermal_coefficient = -0.1
zone_temperature = output[4]
if zone_temperature < self.temp_sp_min:
temp_penalty = self.temp_sp_min - zone_temperature
elif zone_temperature > self.temp_sp_max:
temp_penalty = zone_temperature - self.temp_sp_max
else :
temp_penalty = -10
reward = thermal_coefficient * temp_penalty
# create next step
next_state = np.array(output)
# increment simulation step count
self.KStep += self.time_interval
# done - end of one episode, when kSteps reaches the maximum steps in an episode
done = False
if self.KStep > self.MAXSTEPS:
done = True
return next_state,reward,done
def reset(self):
self.KStep = 0
# initialize all the values of a state
initial_rat = random.uniform(23,27)
initial_sat = random.uniform(12,14)
# return a state
return np.array([self.KStep,self.zone_volume,
rom.load(self.KStep),initial_sat,initial_rat])
I want to read data from csv file in tensorflow .So I've been trying out different ways of reading a CSV file with 2000 lines and each line with 93 features,and I hope to get one-hot value.
my dataset is like this:
the first column is data of 93 features,and the second column is labels of 16 one-hot .
this is my code
import tensorflow as tf
# data_input = pd.read_csv('ans_string.csv')
# data_train = pd.read_csv('ans_result.csv')
x = tf.placeholder(tf.float32,[None,93])
W = tf.Variable(tf.zeros([93,16]))
b = tf.Variable(tf.zeros([16]))
sess = tf.InteractiveSession()
filename_queue = tf.train.string_input_producer(["dataset.csv"])
reader = tf.TextLineReader()
key,value = reader.read(filename_queue)
# _,csv_row = reader.read(filename_queue)
# data = tf.decode_csv(csv_row,record_fefaults = rDeraults)
record_defaults_key = [[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1]]
record_defaults_value = [[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1]]
list_result_key = tf.decode_csv(key,record_defaults = record_defaults_key)
list_result_value = tf.decode_csv(value,record_defaults = record_defaults_value)
features = tf.stack(list_result_key)
labels = tf.stack(list_result_value)
y = tf.nn.softmax(tf.matmul(x,W)+b)
y_ = tf.placeholder(tf.float32,[None,16])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y),reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
with tf.Session() as sess:
# something happened
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord = coord)
tf.global_variables_initializer().run()
for _ in range (1000):
example,label = sess.run([features,labels])
print(sess.run(example,label))
sess.run(train_step,feed_dict={x:example,y_:label})
correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
print(sess.run(accuracy.eval({x:example,y_:label})))
coord.request_stop()
coord.join(threads)
I want to train my model,but I got Error like this.
How can I fix it?
I need to read data in my query with utf8 format, I tried to change collation of my SQL database when I read data base on English alphabet every thing good, but I have trouble in Arabic or other languages.
I print a string stored in variable came from in mysql query and show me like this ???????
how I can solve this problem to show them correct?
After retrieving UTF-8 strings from database, you should manually convert them to CP1256.
You can use function str:fromutf8() defined below
local char, byte, pairs, floor = string.char, string.byte, pairs, math.floor
local table_insert, table_concat = table.insert, table.concat
local unpack = table.unpack or unpack
local function unicode_to_utf8(code)
-- converts numeric UTF code (U+code) to UTF-8 string
local t, h = {}, 128
while code >= h do
t[#t+1] = 128 + code%64
code = floor(code/64)
h = h > 32 and 32 or h/2
end
t[#t+1] = 256 - 2*h + code
return char(unpack(t)):reverse()
end
local function utf8_to_unicode(utf8str, pos)
-- pos = starting byte position inside input string (default 1)
pos = pos or 1
local code, size = utf8str:byte(pos), 1
if code >= 0xC0 and code < 0xFE then
local mask = 64
code = code - 128
repeat
local next_byte = utf8str:byte(pos + size) or 0
if next_byte >= 0x80 and next_byte < 0xC0 then
code, size = (code - mask - 2) * 64 + next_byte, size + 1
else
code, size = utf8str:byte(pos), 1
end
mask = mask * 32
until code < mask
end
-- returns code, number of bytes in this utf8 char
return code, size
end
local map_1256_to_unicode = {
[0x80] = 0x20AC,
[0x81] = 0x067E,
[0x82] = 0x201A,
[0x83] = 0x0192,
[0x84] = 0x201E,
[0x85] = 0x2026,
[0x86] = 0x2020,
[0x87] = 0x2021,
[0x88] = 0x02C6,
[0x89] = 0x2030,
[0x8A] = 0x0679,
[0x8B] = 0x2039,
[0x8C] = 0x0152,
[0x8D] = 0x0686,
[0x8E] = 0x0698,
[0x8F] = 0x0688,
[0x90] = 0x06AF,
[0x91] = 0x2018,
[0x92] = 0x2019,
[0x93] = 0x201C,
[0x94] = 0x201D,
[0x95] = 0x2022,
[0x96] = 0x2013,
[0x97] = 0x2014,
[0x98] = 0x06A9,
[0x99] = 0x2122,
[0x9A] = 0x0691,
[0x9B] = 0x203A,
[0x9C] = 0x0153,
[0x9D] = 0x200C,
[0x9E] = 0x200D,
[0x9F] = 0x06BA,
[0xA0] = 0x00A0,
[0xA1] = 0x060C,
[0xA2] = 0x00A2,
[0xA3] = 0x00A3,
[0xA4] = 0x00A4,
[0xA5] = 0x00A5,
[0xA6] = 0x00A6,
[0xA7] = 0x00A7,
[0xA8] = 0x00A8,
[0xA9] = 0x00A9,
[0xAA] = 0x06BE,
[0xAB] = 0x00AB,
[0xAC] = 0x00AC,
[0xAD] = 0x00AD,
[0xAE] = 0x00AE,
[0xAF] = 0x00AF,
[0xB0] = 0x00B0,
[0xB1] = 0x00B1,
[0xB2] = 0x00B2,
[0xB3] = 0x00B3,
[0xB4] = 0x00B4,
[0xB5] = 0x00B5,
[0xB6] = 0x00B6,
[0xB7] = 0x00B7,
[0xB8] = 0x00B8,
[0xB9] = 0x00B9,
[0xBA] = 0x061B,
[0xBB] = 0x00BB,
[0xBC] = 0x00BC,
[0xBD] = 0x00BD,
[0xBE] = 0x00BE,
[0xBF] = 0x061F,
[0xC0] = 0x06C1,
[0xC1] = 0x0621,
[0xC2] = 0x0622,
[0xC3] = 0x0623,
[0xC4] = 0x0624,
[0xC5] = 0x0625,
[0xC6] = 0x0626,
[0xC7] = 0x0627,
[0xC8] = 0x0628,
[0xC9] = 0x0629,
[0xCA] = 0x062A,
[0xCB] = 0x062B,
[0xCC] = 0x062C,
[0xCD] = 0x062D,
[0xCE] = 0x062E,
[0xCF] = 0x062F,
[0xD0] = 0x0630,
[0xD1] = 0x0631,
[0xD2] = 0x0632,
[0xD3] = 0x0633,
[0xD4] = 0x0634,
[0xD5] = 0x0635,
[0xD6] = 0x0636,
[0xD7] = 0x00D7,
[0xD8] = 0x0637,
[0xD9] = 0x0638,
[0xDA] = 0x0639,
[0xDB] = 0x063A,
[0xDC] = 0x0640,
[0xDD] = 0x0641,
[0xDE] = 0x0642,
[0xDF] = 0x0643,
[0xE0] = 0x00E0,
[0xE1] = 0x0644,
[0xE2] = 0x00E2,
[0xE3] = 0x0645,
[0xE4] = 0x0646,
[0xE5] = 0x0647,
[0xE6] = 0x0648,
[0xE7] = 0x00E7,
[0xE8] = 0x00E8,
[0xE9] = 0x00E9,
[0xEA] = 0x00EA,
[0xEB] = 0x00EB,
[0xEC] = 0x0649,
[0xED] = 0x064A,
[0xEE] = 0x00EE,
[0xEF] = 0x00EF,
[0xF0] = 0x064B,
[0xF1] = 0x064C,
[0xF2] = 0x064D,
[0xF3] = 0x064E,
[0xF4] = 0x00F4,
[0xF5] = 0x064F,
[0xF6] = 0x0650,
[0xF7] = 0x00F7,
[0xF8] = 0x0651,
[0xF9] = 0x00F9,
[0xFA] = 0x0652,
[0xFB] = 0x00FB,
[0xFC] = 0x00FC,
[0xFD] = 0x200E,
[0xFE] = 0x200F,
[0xFF] = 0x06D2,
}
local map_unicode_to_1256 = {}
for code1256, code in pairs(map_1256_to_unicode) do
map_unicode_to_1256[code] = code1256
end
function string.fromutf8(utf8str)
local pos, result_1256 = 1, {}
while pos <= #utf8str do
local code, size = utf8_to_unicode(utf8str, pos)
pos = pos + size
code = code < 128 and code or map_unicode_to_1256[code] or ('?'):byte()
table_insert(result_1256, char(code))
end
return table_concat(result_1256)
end
function string.toutf8(str1256)
local result_utf8 = {}
for pos = 1, #str1256 do
local code = str1256:byte(pos)
table_insert(result_utf8, unicode_to_utf8(map_1256_to_unicode[code] or code))
end
return table_concat(result_utf8)
end
Usage is:
str:fromutf8() -- to convert from UTF-8 to cp1256
str:toutf8() -- to convert from cp1256 to UTF-8
Example:
-- This is cp1256 string
local str1256 = "1\128" -- "one euro" in cp1256
-- Convert it to UTF-8
local str_utf8 = str1256:toutf8() -- "1\226\130\172" -- one euro in utf-8
-- Convert it back from UTF-8 to cp1256
local str1256_2 = str_utf8:fromutf8()
The acc gyro in model.fit is (200 * 3)，in the Input layer shape is (200 * 3). Why is there such a problem? Error when checking input: expected acc_input to have 3 dimensions, but got array with shape (200, 3).This is a visualization of my model.
Here's my code：
WIDE = 20
FEATURE_DIM = 30
CHANNEL = 1
CONV_NUM = 64
CONV_LEN = 3
CONV_LEN_INTE = 3#4
CONV_LEN_LAST = 3#5
CONV_NUM2 = 64
CONV_MERGE_LEN = 8
CONV_MERGE_LEN2 = 6
CONV_MERGE_LEN3 = 4
rnn_size=128
acc_input_tensor = Input(shape=(200,3),name = 'acc_input')
gyro_input_tensor = Input(shape=(200,3),name= 'gyro_input')
Acc_input_tensor = Reshape(target_shape=(20,30,1))(acc_input_tensor)
Gyro_input_tensor = Reshape(target_shape=(20,30,1))(gyro_input_tensor)
acc_conv1 = Conv2D(CONV_NUM,(1, 1*3*CONV_LEN),strides= (1,1*3),padding='valid',activation=None)(Acc_input_tensor)
acc_conv1 = BatchNormalization(axis=1)(acc_conv1)
acc_conv1 = Activation('relu')(acc_conv1)
acc_conv1 = Dropout(0.2)(acc_conv1)
acc_conv2 = Conv2D(CONV_NUM,(1,CONV_LEN_INTE),strides= (1,1),padding='valid',activation=None)(acc_conv1)
acc_conv2 = BatchNormalization(axis=1)(acc_conv2)
acc_conv2 = Activation('relu')(acc_conv2)
acc_conv2 = Dropout(0.2)(acc_conv2)
acc_conv3 = Conv2D(CONV_NUM,(1,CONV_LEN_LAST),strides=(1,1),padding='valid',activation=None)(acc_conv2)
acc_conv3 = BatchNormalization(axis=1)(acc_conv3)
acc_conv3 = Activation('relu')(acc_conv3)
acc_conv3 = Dropout(0.2)(acc_conv3)
gyro_conv1 = Conv2D(CONV_NUM,(1, 1*3*CONV_LEN),strides=(1,1*3),padding='valid',activation=None)(Gyro_input_tensor)
gyro_conv1 = BatchNormalization(axis=1)(gyro_conv1)
gyro_conv1 = Activation('relu')(gyro_conv1)
gyro_conv1 = Dropout(0.2)(gyro_conv1)
gyro_conv2 = Conv2D(CONV_NUM,(1, CONV_LEN_INTE),strides=(1,1),padding='valid',activation=None)(gyro_conv1)
gyro_conv2 = BatchNormalization(axis=1)(gyro_conv2)
gyro_conv2 = Activation('relu')(gyro_conv2)
gyro_conv2 = Dropout(0.2)(gyro_conv2)
gyro_conv3 = Conv2D(CONV_NUM,(1, CONV_LEN_LAST),strides=(1,1),padding='valid',activation=None)(gyro_conv2)
gyro_conv3 = BatchNormalization(axis=1)(gyro_conv3)
gyro_conv3 = Activation('relu')(gyro_conv3)
gyro_conv3 = Dropout(0.2)(gyro_conv3)
sensor_conv_in = concatenate([acc_conv3, gyro_conv3], 2)
sensor_conv_in = Dropout(0.2)(sensor_conv_in)
sensor_conv1 = Conv2D(CONV_NUM2,kernel_size=(2, CONV_MERGE_LEN),padding='SAME')(sensor_conv_in)
sensor_conv1 = BatchNormalization(axis=1)(sensor_conv1)
sensor_conv1 = Activation('relu')(sensor_conv1)
sensor_conv1 = Dropout(0.2)(sensor_conv1)
sensor_conv2 = Conv2D(CONV_NUM2,kernel_size=(2, CONV_MERGE_LEN2),padding='SAME')(sensor_conv1)
sensor_conv2 = BatchNormalization(axis=1)(sensor_conv2)
sensor_conv2 = Activation('relu')(sensor_conv2)
sensor_conv2 = Dropout(0.2)(sensor_conv2)
sensor_conv3 = Conv2D(CONV_NUM2,kernel_size=(2, CONV_MERGE_LEN3),padding='SAME')(sensor_conv2)
sensor_conv3 = BatchNormalization(axis=1)(sensor_conv3)
sensor_conv3 = Activation('relu')(sensor_conv3)
conv_shape = sensor_conv3.get_shape()
print conv_shape
x1 = Reshape(target_shape=(int(conv_shape[1]), int(conv_shape[2]*conv_shape[3])))(sensor_conv3)
x1 = Dense(64, activation='relu')(x1)
gru_1 = GRU(rnn_size, return_sequences=True, init='he_normal', name='gru1')(x1)
gru_1b = GRU(rnn_size, return_sequences=True, go_backwards=True, init='he_normal', name='gru1_b')(x1)
gru1_merged = merge([gru_1, gru_1b], mode='sum')
gru_2 = GRU(rnn_size, return_sequences=True, init='he_normal', name='gru2')(gru1_merged)
gru_2b = GRU(rnn_size, return_sequences=True, go_backwards=True, init='he_normal', name='gru2_b')(gru1_merged)
x = merge([gru_2, gru_2b], mode='concat')
x = Dropout(0.25)(x)
n_class=2
x = Dense(n_class)(x)
model = Model(input=[acc_input_tensor,gyro_input_tensor], output=x)
model.compile(loss='mean_squared_error',optimizer='adam')
model.fit(inputs=[acc,gyro],outputs=labels,batch_size=1, validation_split=0.2, epochs=2,verbose=1 ,
shuffle=False)
The acc gyro in model.fit is (200 * 3)，in the Input layer shape is (200 * 3). Why is there such a problem? Error when checking input: expected acc_input to have 3 dimensions, but got array with shape (200, 3)
Shape (None, 200, 3) is used in Keras for batches, None means batch_size, because in the time of creating or reshaping input arrays, the batch size might be unknown, so if you will be using batch_size = 128 your batch input matrix will have shape (128, 200, 3)
I am using the standard version in RH5.5
These are the only tcl file (sample.tcl) contents:
##general procs used for CCK coding
#use set varname rather than return $varname
#\code
##Documented proc DlgLgrep \c
#accepts a list, and an expression. Returns a list of all elements for list,which match expression
#The expression is carried out in the DlgLgrep contect and local namespace. You can, of course use
#upvar and uplevel within your code.
#for example, let us say that you want numbers that are above 3
#puts [ DlgLgrep { $grepy > 3 } { 1 4 0 8 -2 } ]
#will yield
#4 8
proc DlgLgrep { expry listy } {
#accepts a list, and an expression. Returns a list of all elements for list,which match expression
#The expression is carried out in the DlgLgrep contect and local namespace. You can, of course use
#upvar and uplevel within your code.
#for example, let us say that you want numbers that are above 3
#puts [ DlgLgrep { $grepy > 3 } { 1 4 0 8 -2 } ]
#will yield
#4 8
set ret {}
foreach grepy $listy {
if { [ expr $expry ] } {
lappend ret $grepy
}
}
return $ret
}
#\endcode
These are the doxygen file contents:
PROJECT_NAME = CCK_DLG
PROJECT_NUMBER =
OUTPUT_DIRECTORY = ./
CREATE_SUBDIRS = NO
OUTPUT_LANGUAGE = English
USE_WINDOWS_ENCODING = NO
BRIEF_MEMBER_DESC = YES
REPEAT_BRIEF = YES
ABBREVIATE_BRIEF =
ALWAYS_DETAILED_SEC = NO
INLINE_INHERITED_MEMB = NO
FULL_PATH_NAMES = YES
STRIP_FROM_PATH =
STRIP_FROM_INC_PATH =
SHORT_NAMES = NO
JAVADOC_AUTOBRIEF = NO
MULTILINE_CPP_IS_BRIEF = NO
DETAILS_AT_TOP = NO
INHERIT_DOCS = YES
SEPARATE_MEMBER_PAGES = NO
TAB_SIZE = 8
ALIASES =
OPTIMIZE_OUTPUT_FOR_C = NO
OPTIMIZE_OUTPUT_JAVA = NO
BUILTIN_STL_SUPPORT = NO
DISTRIBUTE_GROUP_DOC = NO
SUBGROUPING = YES
EXTRACT_ALL = YES
EXTRACT_PRIVATE = NO
EXTRACT_STATIC = NO
EXTRACT_LOCAL_CLASSES = YES
EXTRACT_LOCAL_METHODS = NO
HIDE_UNDOC_MEMBERS = NO
HIDE_UNDOC_CLASSES = NO
HIDE_FRIEND_COMPOUNDS = NO
HIDE_IN_BODY_DOCS = NO
INTERNAL_DOCS = NO
CASE_SENSE_NAMES = YES
HIDE_SCOPE_NAMES = NO
SHOW_INCLUDE_FILES = YES
INLINE_INFO = YES
SORT_MEMBER_DOCS = YES
SORT_BRIEF_DOCS = NO
SORT_BY_SCOPE_NAME = NO
GENERATE_TODOLIST = YES
GENERATE_TESTLIST = YES
GENERATE_BUGLIST = YES
GENERATE_DEPRECATEDLIST= YES
ENABLED_SECTIONS =
MAX_INITIALIZER_LINES = 30
SHOW_USED_FILES = YES
SHOW_DIRECTORIES = NO
FILE_VERSION_FILTER =
QUIET = NO
WARNINGS = YES
WARN_IF_UNDOCUMENTED = YES
WARN_IF_DOC_ERROR = YES
WARN_NO_PARAMDOC = NO
WARN_FORMAT = "$file:$line: $text"
WARN_LOGFILE =
INPUT = ./
FILE_PATTERNS =
RECURSIVE = NO
EXCLUDE = *.tcl
EXCLUDE_SYMLINKS = NO
EXCLUDE_PATTERNS =
EXAMPLE_PATH =
EXAMPLE_PATTERNS =
EXAMPLE_RECURSIVE = NO
IMAGE_PATH =
INPUT_FILTER =
FILTER_PATTERNS =
FILTER_SOURCE_FILES = NO
SOURCE_BROWSER = NO
INLINE_SOURCES = NO
STRIP_CODE_COMMENTS = YES
REFERENCED_BY_RELATION = YES
REFERENCES_RELATION = YES
REFERENCES_LINK_SOURCE = YES
USE_HTAGS = NO
VERBATIM_HEADERS = YES
ALPHABETICAL_INDEX = NO
COLS_IN_ALPHA_INDEX = 5
IGNORE_PREFIX =
GENERATE_HTML = YES
HTML_OUTPUT = html
HTML_FILE_EXTENSION = .html
HTML_HEADER =
HTML_FOOTER =
HTML_STYLESHEET =
HTML_ALIGN_MEMBERS = YES
GENERATE_HTMLHELP = NO
CHM_FILE =
HHC_LOCATION =
GENERATE_CHI = NO
BINARY_TOC = NO
TOC_EXPAND = NO
DISABLE_INDEX = NO
ENUM_VALUES_PER_LINE = 4
GENERATE_TREEVIEW = NO
TREEVIEW_WIDTH = 250
GENERATE_LATEX = YES
LATEX_OUTPUT = latex
LATEX_CMD_NAME = latex
MAKEINDEX_CMD_NAME = makeindex
COMPACT_LATEX = NO
PAPER_TYPE = a4wide
EXTRA_PACKAGES =
LATEX_HEADER =
PDF_HYPERLINKS = NO
USE_PDFLATEX = NO
LATEX_BATCHMODE = NO
LATEX_HIDE_INDICES = NO
GENERATE_RTF = NO
RTF_OUTPUT = rtf
COMPACT_RTF = NO
RTF_HYPERLINKS = NO
RTF_STYLESHEET_FILE =
RTF_EXTENSIONS_FILE =
GENERATE_MAN = NO
MAN_OUTPUT = man
MAN_EXTENSION = .3
MAN_LINKS = NO
GENERATE_XML = NO
XML_OUTPUT = xml
XML_SCHEMA =
XML_DTD =
XML_PROGRAMLISTING = YES
GENERATE_AUTOGEN_DEF = NO
GENERATE_PERLMOD = NO
PERLMOD_LATEX = NO
PERLMOD_PRETTY = YES
PERLMOD_MAKEVAR_PREFIX =
ENABLE_PREPROCESSING = YES
MACRO_EXPANSION = NO
EXPAND_ONLY_PREDEF = NO
SEARCH_INCLUDES = YES
INCLUDE_PATH =
INCLUDE_FILE_PATTERNS =
PREDEFINED =
EXPAND_AS_DEFINED =
SKIP_FUNCTION_MACROS = YES
TAGFILES =
GENERATE_TAGFILE =
ALLEXTERNALS = NO
EXTERNAL_GROUPS = YES
PERL_PATH = /usr/bin/perl
CLASS_DIAGRAMS = YES
HIDE_UNDOC_RELATIONS = YES
HAVE_DOT = NO
CLASS_GRAPH = YES
COLLABORATION_GRAPH = YES
GROUP_GRAPHS = YES
UML_LOOK = NO
TEMPLATE_RELATIONS = NO
INCLUDE_GRAPH = YES
INCLUDED_BY_GRAPH = YES
CALL_GRAPH = NO
CALLER_GRAPH = NO
GRAPHICAL_HIERARCHY = YES
DIRECTORY_GRAPH = YES
DOT_IMAGE_FORMAT = png
DOT_PATH =
DOTFILE_DIRS =
MAX_DOT_GRAPH_WIDTH = 1024
MAX_DOT_GRAPH_HEIGHT = 1024
MAX_DOT_GRAPH_DEPTH = 0
DOT_TRANSPARENT = NO
DOT_MULTI_TARGETS = NO
GENERATE_LEGEND = YES
DOT_CLEANUP = YES
SEARCHENGINE = NO
this is a small excerpt, of course. I am a newbie, and no-one where I work has done any doxygen work.
Thanks!